emerchantpay

• Define and maintain the information security strategy, standards, and roadmap, aligned to applicable regulations, rules, and security best practices. • Steer security architecture across a cloud-native environment, defining secure-by-design patterns for microservices, APIs, and shared platform services. • Establish and govern secure software development lifecycle (secure SDLC) practices, embedding automated security controls into CI/CD pipelines. • Define and drive adoption of cloud security guardrails - identity, network segmentation, encryption, secrets management, and configuration baselines. • Build and run security monitoring, logging, and threat detection across cloud, infrastructure, and application layers. • Lead the security incident response lifecycle - preparation, detection, containment, eradication, recovery, and post-incident review - and act as incident commander for security events. • Own vulnerability and threat management: scanning, risk-based prioritization, remediation tracking, and reporting across infrastructure, containers, and application code. • Plan and coordinate penetration testing and offensive-security exercises (in-house or co-sourced) and drive findings to closure. • Govern identity and access management, privileged access, and least-privilege principles across cloud and corporate systems. • Define and oversee data protection controls - encryption, key management, data classification, and loss prevention - for sensitive and cardholder data. • Secure corporate IT and office infrastructure, including endpoints, networks, and productivity and collaboration platforms. • Partner with Engineering and DevOps teams to make the secure path the easy path, providing tooling, standards, threat modelling, and design reviews. • Provide security input into architecture and change decisions, including the adoption of new technologies and third-party services. • Run security awareness and phishing-resilience programs for technical and non-technical staff. • Implement and evidence the technical security controls underpinning PCI DSS, ISO 27001, and SOC audits. • Monitor the evolving threat landscape and emerging security technologies. • Act as a key member of the internal security center of excellence and contribute to cross-functional security working groups. • Build, lead, and mentor a small security team. • Report security posture, key risks, and metrics.

• Define and maintain the information security strategy, standards, and roadmap, aligned to applicable regulations, rules, and security best practices. • Steer security architecture across a cloud-native environment, defining secure-by-design patterns for microservices, APIs, and shared platform services. • Establish and govern secure software development lifecycle (secure SDLC) practices, embedding automated security controls into CI/CD pipelines. • Define and drive adoption of cloud security guardrails - identity, network segmentation, encryption, secrets management, and configuration baselines. • Build and run security monitoring, logging, and threat detection across cloud, infrastructure, and application layers. • Lead the security incident response lifecycle - preparation, detection, containment, eradication, recovery, and post-incident review - and act as incident commander for security events. • Own vulnerability and threat management: scanning, risk-based prioritization, remediation tracking, and reporting across infrastructure, containers, and application code. • Plan and coordinate penetration testing and offensive-security exercises (in-house or co-sourced) and drive findings to closure. • Govern identity and access management, privileged access, and least-privilege principles across cloud and corporate systems. • Define and oversee data protection controls - encryption, key management, data classification, and loss prevention - for sensitive and cardholder data. • Secure corporate IT and office infrastructure, including endpoints, networks, and productivity and collaboration platforms. • Partner with Engineering and DevOps teams to make the secure path the easy path, providing tooling, standards, threat modelling, and design reviews. • Provide security input into architecture and change decisions, including the adoption of new technologies and third-party services. • Run security awareness and phishing-resilience programs for technical and non-technical staff. • Implement and evidence the technical security controls underpinning PCI DSS, ISO 27001, and SOC audits. • Monitor the evolving threat landscape and emerging security technologies. • Act as a key member of the internal security center of excellence and contribute to cross-functional security working groups. • Build, lead, and mentor a small security team. • Report security posture, key risks, and metrics.

Role Description We are looking for an experienced Information Security Lead to own the design, implementation, and continuous improvement of information security across our cloud-native, DevOps-driven engineering environment, as well as our corporate IT and office infrastructure. The role combines hands-on technical delivery with security leadership. - Define and maintain the information security strategy, standards, and roadmap, aligned to applicable regulations, rules, and security best practices. - Steer security architecture across a cloud-native environment, defining secure-by-design patterns for microservices, APIs, and shared platform services. - Establish and govern secure software development lifecycle (secure SDLC) practices, embedding automated security controls into CI/CD pipelines. - Define and drive adoption of cloud security guardrails - identity, network segmentation, encryption, secrets management, and configuration baselines. - Build and run security monitoring, logging, and threat detection across cloud, infrastructure, and application layers. - Lead the security incident response lifecycle - preparation, detection, containment, eradication, recovery, and post-incident review - and act as incident commander for security events. - Own vulnerability and threat management: scanning, risk-based prioritization, remediation tracking, and reporting across infrastructure, containers, and application code. - Plan and coordinate penetration testing and offensive-security exercises (in-house or co-sourced) and drive findings to closure. - Govern identity and access management, privileged access, and least-privilege principles across cloud and corporate systems. - Define and oversee data protection controls - encryption, key management, data classification, and loss prevention - for sensitive and cardholder data. - Secure corporate IT and office infrastructure, including endpoints, networks, and productivity and collaboration platforms. - Partner with Engineering and DevOps teams to make the secure path the easy path, providing tooling, standards, threat modelling, and design reviews. - Provide security input into architecture and change decisions, including the adoption of new technologies and third-party services. - Run security awareness and phishing-resilience programs for technical and non-technical staff. - Implement and evidence the technical security controls underpinning PCI DSS, ISO 27001, and SOC audits. - Monitor the evolving threat landscape and emerging security technologies. - Act as a key member of the internal security center of excellence and contribute to cross-functional security working groups. - Build, lead, and mentor a small security team. - Report security posture, key risks, and metrics. Qualifications - Bachelor’s or master’s degree in computer science, information security, or a related field, or equivalent practical experience. - At least 10 years in information / cyber security, including a minimum of 2-3 years in a leadership role, with hands-on experience securing cloud-native environments at scale. - Deep, practical public-cloud security knowledge (AWS strongly preferred): identity, networking, encryption, logging, and configuration management. - Strong experience securing DevOps / CI/CD pipelines and modern microservices architectures - containers, APIs, and infrastructure-as-code. - Working knowledge of application security and secure SDLC across modern programming languages and web frameworks. - Hands-on experience with security operations, incident response, and vulnerability management. - Solid understanding of security frameworks and compliance standards relevant to payments: ISO 27001, PCI DSS, SOC 2, and NIST CSF. - Working AI security literacy, with hands-on use of AI-assisted security tooling (e.g., GenAI coding assistants, AI-augmented SAST/DAST and SIEM/SOC analytics) and a practical understanding of securing AI/LLM and agentic applications, including AWS AI services such as Amazon Bedrock and the OWASP Top 10 risks for LLMs (e.g., prompt injection and data leakage). - Strong analytical and problem-solving ability, with high integrity and sound judgement. - Excellent verbal and written communication skills, fluent English, and the ability to influence engineers with data, logic, and best practices. Requirements - Professional certification such as CISSP, CCSP, OSCP, AWS Security Specialty, or CISM. - Experience in a payments, fintech, banking, or other regulated environment. - Familiarity with operational-resilience expectations (e.g. DORA-style requirements). - Experience standing up a security function. Benefits - Fast-growing payment company; - Excellent working conditions, casual atmosphere, and state-of-the-art hardware; - Modern, challenging, constantly growing business; - Professional development - books, trainings, certifications, etc.; - Team buildings and fun activities; - 25 days paid holiday, 1 day for every 2 years with us; - Fully distributed and remote.

Role Description We are looking for an IT Governance, Risk, and Compliance Manager to provide oversight of our ICT and information security risk profile, ensuring those risks are identified, managed, and reported within the company's risk appetite, and that governance, risk management, compliance, and resilience are embedded into the way the company operates and grows. The role owns the integrated control framework, multi-standard certifications (ISO 27001, PCI DSS, and SOC), enterprise and third-party risk, business continuity, and key regulatory readiness programs - including the RBI licensing application in India, NIS 2, and the EU AI Act for AI governance and compliance - while acting as a trusted advisor to the Leadership Team. The role sits within the IT function and is part of the Risk Management and Oversight Committee. It works closely with Engineering, IT, Legal, Finance, and the wider business. Responsibilities - Define and maintain the information security strategy, standards, and roadmap, aligned to applicable regulations, rules, and security best practices. - Steer security architecture across a cloud-native environment, defining secure-by-design patterns for microservices, APIs, and shared platform services. - Establish and govern secure software development lifecycle (secure SDLC) practices, embedding automated security controls into CI/CD pipelines. - Define and drive adoption of cloud security guardrails - identity, network segmentation, encryption, secrets management, and configuration baselines. - Build and run security monitoring, logging, and threat detection across cloud, infrastructure, and application layers. - Lead the security incident response lifecycle - preparation, detection, containment, eradication, recovery, and post-incident review - and act as incident commander for security events. - Own vulnerability and threat management: scanning, risk-based prioritization, remediation tracking, and reporting across infrastructure, containers, and application code. - Plan and coordinate penetration testing and offensive-security exercises (in-house or co-sourced) and drive findings to closure. - Govern identity and access management, privileged access, and least-privilege principles across cloud and corporate systems. - Define and oversee data protection controls - encryption, key management, data classification, and loss prevention - for sensitive and cardholder data. - Secure corporate IT and office infrastructure, including endpoints, networks, and productivity and collaboration platforms. - Partner with Engineering and DevOps teams to make the secure path the easy path, providing tooling, standards, threat modelling, and design reviews. - Provide security input into architecture and change decisions, including the adoption of new technologies and third-party services. - Run security awareness and phishing-resilience programs for technical and non-technical staff. - Implement and evidence the technical security controls underpinning PCI DSS, ISO 27001, and SOC audits. - Monitor the evolving threat landscape and emerging security technologies. - Act as a key member of the internal security center of excellence and contribute to cross-functional security working groups. - Build, lead, and mentor a small security team. - Report security posture, key risks, and metrics. Qualifications - Bachelor’s or master’s degree in computer science, information security, or a related field, or equivalent practical experience. - At least 10 years in information / cyber security, including a minimum of 2-3 years in a leadership role, with hands-on experience securing cloud-native environments at scale. - Deep, practical public-cloud security knowledge (AWS strongly preferred): identity, networking, encryption, logging, and configuration management. - Strong experience securing DevOps / CI/CD pipelines and modern microservices architectures - containers, APIs, and infrastructure-as-code. - Working knowledge of application security and secure SDLC across modern programming languages and web frameworks. - Hands-on experience with security operations, incident response, and vulnerability management. - Solid understanding of security frameworks and compliance standards relevant to payments: ISO 27001, PCI DSS, SOC 2, and NIST CSF. - Working AI security literacy, with hands-on use of AI-assisted security tooling (e.g., GenAI coding assistants, AI-augmented SAST/DAST and SIEM/SOC analytics) and a practical understanding of securing AI/LLM and agentic applications, including AWS AI services such as Amazon Bedrock and the OWASP Top 10 risks for LLMs (e.g., prompt injection and data leakage). - Strong analytical and problem-solving ability, with high integrity and sound judgement. - Excellent verbal and written communication skills, fluent English, and the ability to influence engineers with data, logic, and best practices. Considered as an Advantage - Professional certification such as CISSP, CCSP, OSCP, AWS Security Specialty, or CISM. - Experience in a payments, fintech, banking, or other regulated environment. - Familiarity with operational-resilience expectations (e.g. DORA-style requirements). - Experience standing up a security function. Benefits - Fast-growing payment company; - Excellent working conditions, casual atmosphere, and state-of-the-art hardware; - Modern, challenging, constantly growing business; - Professional development - books, trainings, certifications, etc.; - Team buildings and fun activities; - 25 days paid holiday, 1 day for every 2 years with us; - Fully distributed and remote.

• Design, create, maintain, and continuously improve test cases for different projects and product areas. • Use AI-assisted tools to generate, review, optimize, and maintain test cases and test scenarios. • Execute different types of black-box testing, including functional, regression, integration, exploratory, and non-functional testing. • Perform API testing and contribute to API test automation across internal and external services. • Monitor, track, analyze, and report defects identified during test execution. • Work closely with developers, product owners, business analysts, and other stakeholders to understand requirements and define appropriate test coverage. • Keep track of development changes and carry out relevant testing when needed. • Automate test cases using various tools, frameworks, and programming/scripting languages. • Use AI tools, coding agents, and MCP-enabled workflows to improve QA productivity, including test generation, automation support, defect analysis, documentation, and integrations. • Contribute to the testing strategy for cloud-native systems, microservices, integrations, and distributed architectures. • Document and assure the quality of software applications across all architectural layers. • Support continuous improvement of QA processes, tools, standards, and best practices.

Role Description We are looking for an experienced, full-time Senior Quality Assurance Engineer to join our IT team. The ideal candidate has strong hands-on experience in software quality assurance, test automation, API testing, and testing of modern cloud-native systems. This role is suitable for someone who can work independently across complex systems, contribute to QA strategy, improve automation coverage, and actively use modern AI tools to increase testing efficiency and quality. - Design, create, maintain, and continuously improve test cases for different projects and product areas. - Use AI-assisted tools to generate, review, optimize, and maintain test cases and test scenarios. - Execute different types of black-box testing, including functional, regression, integration, exploratory, and non-functional testing. - Perform API testing and contribute to API test automation across internal and external services. - Monitor, track, analyze, and report defects identified during test execution. - Work closely with developers, product owners, business analysts, and other stakeholders to understand requirements and define appropriate test coverage. - Keep track of development changes and carry out relevant testing when needed. - Automate test cases using various tools, frameworks, and programming/scripting languages. - Use AI tools, coding agents, and MCP-enabled workflows to improve QA productivity, including test generation, automation support, defect analysis, documentation, and integrations. - Contribute to the testing strategy for cloud-native systems, microservices, integrations, and distributed architectures. - Document and assure the quality of software applications across all architectural layers. - Support continuous improvement of QA processes, tools, standards, and best practices. Qualifications - 7+ years of experience in Software Quality Assurance. - 3+ years of experience in automated testing — developer profiles welcome! - Knowledge of Ruby, Rails, Python, JavaScript/TypeScript, or other programming languages used for test automation. - Strong experience with test case design, test execution, defect reporting, regression testing, and release validation. - Strong hands-on experience with test automation tools and frameworks, such as Playwright, Selenium, Cypress, or similar. - Experience with API testing and API automation using tools or frameworks such as Postman, REST Assured, Playwright API testing, or similar. - Good understanding of the SDLC, Agile delivery practices, CI/CD processes, and modern software engineering workflows. - Comprehensive knowledge of web technologies, such as HTML, CSS, JavaScript, browser behavior, HTTP/HTTPS, REST APIs, and related protocols. - Strong knowledge of AWS, including practical experience with cloud-native systems and complex microservices testing. - Practical experience using AI tools in QA activities, including AI-assisted test case creation, test automation, test data generation, defect investigation, documentation, and coding-agent-assisted workflows. - Experience using MCP-enabled tools and integrations, such as Playwright MCP, Atlassian MCP, or similar AI-assisted development and QA workflows. - Ability to understand complex systems, identify risk areas, and define effective testing approaches. - Highly proficient in spoken and written English. - Enthusiastic, hard-working, motivated individual with excellent communication skills. Requirements - Experience with Jenkins, GitHub Actions, GitLab CI, or other CI/CD systems. - Experience with Linux environments. - Experience with mobile testing. - Experience testing payment systems, fintech platforms, or other high-availability transactional systems. - ISTQB Foundation or higher-level certification. - Experience contributing to QA strategy, automation frameworks, or test infrastructure design. Benefits - Fast-growing payment company; - Excellent working conditions, casual atmosphere, and state-of-the-art hardware; - Modern, challenging, constantly growing business; - Professional development – books, trainings, certifications, etc.; - Team buildings and fun activities; - 25 days paid holiday, 1 day for every 2 years with us; - Fully distributed and remote.

• Design, build, and maintain AI-powered applications, services, and integrations as part of the AI Engineering team. • Implement solutions focused on AI agents, agentic workflows, automation, LLM-based applications, and AI-assisted business processes. • Build and integrate AI applications using technologies such as Python (FastAPI/Flask/Django) or equivalent frameworks, React frontends, and relevant AI/ML frameworks. • Implement AI solutions using AWS AI/ML services, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and other AWS services for model hosting, inference, orchestration, data processing, monitoring, and security. • Work closely with the AI Tech Lead to align on architecture, technology choices, engineering standards, AI patterns, and rollout approaches. • Provide technical input and guidance to other engineers on AI implementation patterns, code quality, testing, observability, and production readiness. • Develop and integrate AI agents that interact with internal APIs, business workflows, enterprise systems, knowledge bases, and external tools in a safe and controlled way. • Build and maintain RAG-based solutions, including document ingestion, chunking, embeddings, vector search, retrieval logic, reranking, and grounding techniques. • Support the development and deployment of machine learning models and AI solutions into production environments. • Contribute to ML pipelines and MLOps practices, including data preparation, model training, experiment tracking, model deployment, monitoring, evaluation, and lifecycle management. • Integrate LLMs through APIs. • Implement AI evaluation approaches for LLM outputs, RAG quality, agent behavior, model performance, hallucination detection, safety, and reliability. • Support prompt engineering, prompt versioning, function calling, tool use, memory patterns, guardrails, and LLM application testing. • Design and consume APIs and contribute to cloud-based, scalable backend architectures. • Collaborate with product managers, engineers, data scientists, DevOps, security, and business stakeholders to deliver practical AI solutions. • Write clean, maintainable, testable, and well-documented code. • Support production rollouts, troubleshooting, monitoring, optimization, and continuous improvement of AI systems. • Stay current with modern AI technologies, frameworks, models, and engineering practices, and bring practical recommendations to the team.

Role Description We are looking for a Senior AI Engineer to join our AI Engineering team and help design, build, and roll out production-grade AI solutions, with a strong focus on AI engineering, AI agents, agentic workflows, machine learning, GenAI, and LLM-based applications. This is a senior individual contributor role within the AI Engineering team. The Senior AI Engineer will work closely with the AI Tech Lead, engineering teams, product stakeholders, data teams, cloud/platform teams, and security teams to deliver reliable AI capabilities into real business systems. The technology stack is diverse and can include Python (FastAPI/Flask/Django) or equivalent frameworks; React on the frontend side, and various ML/AI frameworks, APIs, cloud-native services, along with modern AI tooling. The role will have a strong focus on AWS, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and other AWS AI/ML services. Responsibilities - Design, build, and maintain AI-powered applications, services, and integrations as part of the AI Engineering team. - Implement solutions focused on AI agents, agentic workflows, automation, LLM-based applications, and AI-assisted business processes. - Build and integrate AI applications using technologies such as Python (FastAPI/Flask/Django) or equivalent frameworks, React frontends, and relevant AI/ML frameworks. - Implement AI solutions using AWS AI/ML services, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and other AWS services for model hosting, inference, orchestration, data processing, monitoring, and security. - Work closely with the AI Tech Lead to align on architecture, technology choices, engineering standards, AI patterns, and rollout approaches. - Provide technical input and guidance to other engineers on AI implementation patterns, code quality, testing, observability, and production readiness. - Develop and integrate AI agents that interact with internal APIs, business workflows, enterprise systems, knowledge bases, and external tools in a safe and controlled way. - Build and maintain RAG-based solutions, including document ingestion, chunking, embeddings, vector search, retrieval logic, reranking, and grounding techniques. - Support the development and deployment of machine learning models and AI solutions into production environments. - Contribute to ML pipelines and MLOps practices, including data preparation, model training, experiment tracking, model deployment, monitoring, evaluation, and lifecycle management. - Integrate LLMs through APIs. - Implement AI evaluation approaches for LLM outputs, RAG quality, agent behavior, model performance, hallucination detection, safety, and reliability. - Support prompt engineering, prompt versioning, function calling, tool use, memory patterns, guardrails, and LLM application testing. - Design and consume APIs and contribute to cloud-based, scalable backend architectures. - Collaborate with product managers, engineers, data scientists, DevOps, security, and business stakeholders to deliver practical AI solutions. - Write clean, maintainable, testable, and well-documented code. - Support production rollouts, troubleshooting, monitoring, optimization, and continuous improvement of AI systems. - Stay current with modern AI technologies, frameworks, models, and engineering practices, and bring practical recommendations to the team. Qualifications - Minimum 7-8 years of professional experience in software engineering, AI engineering, ML engineering, data science, or related technical roles. - At least 2-3 years of experience in AI development, ML engineering, or data science, with a demonstrated track record of deploying machine learning models and AI solutions in production environments. - Strong hands-on experience building production-grade AI, ML, and data-driven systems. - Practical experience with AI agents, agentic workflows, LLM-based applications, tool-calling architectures, workflow automation, and AI orchestration patterns. - Strong understanding of modern AI concepts, including deep learning, generative AI, LLMs, embeddings, RAG, LLM fine-tuning, and AI evaluation. - Strong Python development experience, including experience with Python (FastAPI/Flask/Django) or equivalent frameworks. - Some experience with React for building user-facing AI tools, internal applications, dashboards, or workflow interfaces. - Strong knowledge of AWS, including practical experience with cloud-native architectures, Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and related AWS AI/ML services (the more, the better). - Experience with advanced LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar agent/orchestration frameworks. - Experience with PyTorch or TensorFlow, and familiarity with Hugging Face Transformers. - Hands-on experience using LLMs via APIs, such as OpenAI, Anthropic, Gemini, or similar providers. - Experience with ML pipelines and MLOps, including data preparation, model training, model deployment, experiment tracking, model/version management, monitoring, evaluation, and production support. - Experience with AI evaluation frameworks, tools, and techniques for assessing LLM outputs, RAG performance, agent behavior, model quality, safety, reliability, and regression over time. - Knowledge or practical experience with RLHF - human-in-the-loop evaluation, preference data, reward modeling, or feedback-driven model improvement. - Experience with vector databases and retrieval/search technologies, such as Amazon OpenSearch, Pinecone, pgvector, or similar. - Experience building RAG systems, including document ingestion, chunking strategies, embeddings, retrieval evaluation, reranking, and grounding techniques. - Experience with model fine-tuning, embedding models, transformer architectures, open-source LLMs, and model benchmarking. - Knowledge of API design, microservices, event-driven systems, and cloud-based architectures. - Good understanding of security and governance requirements for AI systems, including access control, secrets management, data privacy, audit logging, and safe handling of sensitive data. - Experience working in cross-functional teams with engineers, product managers, data scientists, DevOps, security, and business stakeholders. - Strong problem-solving skills and ability to turn AI prototypes into reliable, maintainable production systems. - Strong communication skills and ability to explain technical decisions clearly to both technical and non-technical stakeholders. Considered as an Advantage - Experience with Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, or similar managed AI capabilities. - Experience with containerization and orchestration, including Docker and EKS/ECS. - Experience with infrastructure as code using Terraform, AWS CDK, or CloudFormation. - Experience with data platforms, ETL/ELT pipelines, data lakes, feature stores, and real-time data processing. - Experience implementing responsible AI controls, AI governance frameworks, safety guardrails, and compliance processes. - Experience with observability for AI systems, including tracing, cost monitoring, prompt/model analytics, latency tracking, and quality dashboards. - Experience integrating AI systems with enterprise platforms, internal APIs, CRM/ERP systems, ticketing systems, knowledge bases, and workflow engines. - Contributions to open-source AI/ML projects, published technical content, conference talks, or patents in AI/ML-related areas. - AWS certifications, especially in architecture, machine learning, security, or DevOps. - Experience in fintech. Benefits - Fast-growing payment company; - Excellent working conditions, casual atmosphere, and state-of-the-art hardware; - Modern, challenging, constantly growing business; - Professional development – books, trainings, certifications, etc.; - Team buildings and fun activities; - 25 days paid holiday, 1 day for every 2 years with us; - Fully distributed and remote.

• Lead the technical design, architecture, and delivery of AI solutions, with a focus on AI agents, agentic workflows, automation, and AI-assisted business processes. • Own the end-to-end engineering lifecycle of AI products: discovery, prototyping, evaluation, production implementation, rollout, monitoring, and continuous improvement. • Lead and manage an AI engineering team, including technical direction, task breakdown, mentoring, code reviews, delivery planning, and engineering quality. • Design and implement solutions using AWS AI/ML services, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and other AWS services for model hosting, orchestration, data processing, monitoring, and security. • Build and integrate AI applications using technologies such as Python (FastAPI/Flask/Django) or equivalent, along with relevant AI/ML frameworks. • Design agentic systems that can interact with APIs, internal platforms, business workflows, knowledge bases, and external tools in a safe, observable, and controlled way. • Define and implement best practices for LLM application development, including prompt engineering, RAG, tool use, function calling, memory, evaluation, guardrails, and hallucination mitigation. • Drive improvements in internal engineering practices around AI-assisted development, engineering productivity, AI efficiency, automation, and responsible use of AI tools across software delivery. • Work with stakeholders to identify high-value AI use cases, assess feasibility, define success metrics, and prioritize delivery. • Establish engineering standards for AI systems, including code quality, testing, observability, reliability, security, scalability, and maintainability. • Drive MLOps and LLMOps practices, including model lifecycle management, deployment pipelines, monitoring, evaluation, drift detection, and rollback strategies. • Collaborate with DevOps, cloud, security, and platform teams to ensure AI systems are production-ready, compliant, cost-efficient, and operationally stable. • Support rollout and adoption of AI solutions across the organization, including documentation, training, stakeholder communication, and production support. • Evaluate emerging AI technologies, frameworks, models, and vendors, and provide pragmatic recommendations on adoption. • Ensure AI solutions follow responsible AI principles, including data privacy, access control, auditability, fairness, explainability where applicable, and secure handling of sensitive data.

Role Description We are looking for an AI Tech Lead to lead the design, engineering, and rollout of AI-powered solutions, with a strong focus on AI engineering, AI agents, agentic workflows, applied machine learning, and production-grade AI systems. The role combines hands-on technical leadership, architecture, delivery ownership, and people leadership. The AI Tech Lead will guide a small but growing AI engineering team and work closely with product, engineering, data, security, infrastructure, and business stakeholders to turn AI opportunities into reliable production systems. The role will have a strong focus on AWS, including advanced AI services such as Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and other AWS AI/ML and cloud-native services. Responsibilities - Lead the technical design, architecture, and delivery of AI solutions, with a focus on AI agents, agentic workflows, automation, and AI-assisted business processes. - Own the end-to-end engineering lifecycle of AI products: discovery, prototyping, evaluation, production implementation, rollout, monitoring, and continuous improvement. - Lead and manage an AI engineering team, including technical direction, task breakdown, mentoring, code reviews, delivery planning, and engineering quality. - Design and implement solutions using AWS AI/ML services, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and other AWS services for model hosting, orchestration, data processing, monitoring, and security. - Build and integrate AI applications using technologies such as Python (FastAPI/Flask/Django) or equivalent, along with relevant AI/ML frameworks. - Design agentic systems that can interact with APIs, internal platforms, business workflows, knowledge bases, and external tools in a safe, observable, and controlled way. - Define and implement best practices for LLM application development, including prompt engineering, RAG, tool use, function calling, memory, evaluation, guardrails, and hallucination mitigation. - Drive improvements in internal engineering practices around AI-assisted development, engineering productivity, AI efficiency, automation, and responsible use of AI tools across software delivery. - Work with stakeholders to identify high-value AI use cases, assess feasibility, define success metrics, and prioritize delivery. - Establish engineering standards for AI systems, including code quality, testing, observability, reliability, security, scalability, and maintainability. - Drive MLOps and LLMOps practices, including model lifecycle management, deployment pipelines, monitoring, evaluation, drift detection, and rollback strategies. - Collaborate with DevOps, cloud, security, and platform teams to ensure AI systems are production-ready, compliant, cost-efficient, and operationally stable. - Support rollout and adoption of AI solutions across the organization, including documentation, training, stakeholder communication, and production support. - Evaluate emerging AI technologies, frameworks, models, and vendors, and provide pragmatic recommendations on adoption. - Ensure AI solutions follow responsible AI principles, including data privacy, access control, auditability, fairness, explainability where applicable, and secure handling of sensitive data. Qualifications - Minimum 10 years of professional experience in software engineering, data engineering, machine learning engineering, AI engineering, or related technical roles. - At least 3 years of experience leading or managing engineering teams, including technical leadership, mentoring, planning, and delivery ownership. - Strong hands-on experience building production-grade AI, ML, and data-driven systems. - Practical experience with AI agents, agentic workflows, LLM-based applications, workflow automation, tool-calling architectures, and AI orchestration patterns. - Strong knowledge of AWS, including practical experience with cloud-native architectures, Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and related AWS AI/ML services (the more, the better). - Build and integrate AI applications using technologies such as Python (FastAPI/Flask/Django), and relevant AI/ML frameworks. - Experience with advanced LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar agent/orchestration frameworks. - Experience building RAG systems, including document ingestion, chunking strategies, embeddings, retrieval evaluation, reranking, and grounding techniques. - Solid understanding of machine learning concepts, including supervised/unsupervised learning, model training, feature engineering, evaluation, inference, and model performance metrics. - Experience with MLOps / LLMOps, including CI/CD for ML and AI applications, model deployment, experiment tracking, model/prompt/version management, monitoring, evaluation pipelines, and production rollback strategies. - Experience with vector databases and retrieval/search technologies, such as Amazon OpenSearch, Pinecone, pgvector, or similar. - Experience with model fine-tuning, embedding models, transformer architectures, open-source LLMs, and model benchmarking. - Experience designing APIs, microservices, event-driven systems, and cloud-native backend architectures. - Strong understanding of security and governance requirements for AI systems, including access control, secrets management, data privacy, audit logging, and safe use of sensitive data. - Experience working with cross-functional teams, including product managers, architects, engineers, data scientists, security teams, and business stakeholders. - Ability to move from prototype to production without creating “AI demo theater” — the system must actually work, scale, and survive contact with real users. - Strong communication skills, with the ability to explain complex AI and engineering topics to both technical and non-technical audiences. - Strong ownership mindset, pragmatic decision-making, and ability to balance innovation with delivery discipline. Considered as an Advantage - Experience with containerization and orchestration, including Docker and EKS/ECS. - Experience with infrastructure as code using Terraform, AWS CDK, or CloudFormation. - Experience with data platforms, ETL/ELT pipelines, data lakes, feature stores, and real-time data processing. - Experience implementing responsible AI controls, AI governance frameworks, safety guardrails, and compliance processes. - Experience integrating AI systems with enterprise platforms, internal APIs, CRM/ERP systems, ticketing systems, knowledge bases, and workflow engines. - Experience managing AI adoption programs, internal AI platforms, or organization-wide AI enablement initiatives. - Contributions to open-source AI/ML projects, published technical content, conference talks, or patents in AI/ML-related areas. - AWS certifications, especially in architecture, machine learning, security, or DevOps. - Experience in fintech. Benefits - Fast-growing payment company; - Excellent working conditions, casual atmosphere, and state-of-the-art hardware; - Modern, challenging, constantly growing business; - Professional development – books, trainings, certifications, etc.; - Team buildings and fun activities; - 25 days paid holiday, 1 day for every 2 years with us; - Fully distributed and remote.