emerchantpay

• 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.

• Be part of a growing Data Analytics team; • Work with the Business, Product, and Engineering teams to determine metrics, reports, and analytics needed for the business; • Design, architect, implement, and support key datasets that provide structured and timely access to actionable business information with the needs of the end user always in view; • Retrieve and analyse data using SQL, Excel, and other data management & BI systems; • Develop queries for ad-hoc requests and projects, as well as ongoing reporting; • Monitor existing metrics and analyse data, answer key business questions in a data-driven way; • Work with the latest AWS cloud technologies.

Role Description We are looking for an outstanding Business Intelligence engineer who is passionate about data-driven decision making, is detail-oriented, self-starter, smart, efficient, and driven to help our business succeed. Knowledge, experience, and desire to work with analytical tools and writing excellent SQL queries is a necessity. - Be part of a growing Data Analytics team; - Work with the Business, Product, and Engineering teams to determine metrics, reports, and analytics needed for the business; - Design, architect, implement, and support key datasets that provide structured and timely access to actionable business information with the needs of the end user always in view; - Retrieve and analyse data using SQL, Excel, and other data management & BI systems; - Develop queries for ad-hoc requests and projects, as well as ongoing reporting; - Monitor existing metrics and analyse data, answer key business questions in a data-driven way; - Work with the latest AWS cloud technologies. Qualifications - 6-7+ years in Data/BI/Analytics/Reporting; - Proven analytical and quantitative ability and a passion for enabling users to use data and metrics to back up assumptions, develop business cases, and complete root-cause analyses; - Strong knowledge in SQL, advanced SQL usage; - Experience with BI solutions - any of QuickSight, Tableau, Qlikview, PowerBI, etc; - Experience with data modelling and reporting; - Experience with different DB engines like PostgreSQL, MySQL, Oracle, SQL Server, etc a definite advantage; - Familiarity and desire to work with latest AWS cloud-based tools and services; - Highly proficient in spoken and written English; - Enthusiastic, hard-working, and motivated team player with excellent communication skills. Requirements - Able to train users in BI concepts and tools; - Experience with automation & programming; - Experience with warehousing; - Experience with ETL tools; - Previous experience in the payment industry. 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.

• Designing and implementing complex workflows, APIs; • Writing scalable, robust, testable, efficient, and easily maintainable code; • Translating software requirements into stable, working, high performance and high-availability software; • Playing a key role in architectural and design decisions, building toward efficient microservices distributed architecture.

• Designing and implementing complex billing features, workflows, APIs, and UI backends for core billing needs • Performing TDD with the appropriate tools • Writing and updating technical documentation • Working with cross-functional and cross-cultural teams and projects within the technology domain

• Lead and mentor the Data Analytics team by overseeing day-to-day activities, setting goals, and performing reviews; • Interface with the Senior Management Team and other senior leadership to identify innovative uses of data and help drive BI and analytics strategy; • Guide the design and implementation of successful data solutions through the effective use of business intelligence tools; • Work with the Business, Product, and Engineering teams to determine metrics, reports, and analytics needed for the business; • Design, architect, implement, and support key datasets that provide structured and timely access to actionable business information with the needs of the end user always in view; • Retrieve and analyse data using SQL, and other data management & BI systems; • Develop queries for ad-hoc requests and projects, as well as ongoing reporting; • Monitor existing metrics and analyse data, answer key business questions in a data-driven way; • Review Data Analytics related processes and documentation for continuous improvement; • Work with the latest AWS cloud technologies;

• To provide professional technical support to clients and partners of the company; • To perform technical administration of various systems such as Payment Gateway, eWallet, CRM, etc.; • To support and monitor the system applications; • To Create and update merchant client accounts in the system; • To assist clients during the integration period with our systems and support integrations to service providers; • To troubleshoot, report, and resolve issues in a timely manner; • To interact with other teams and clients to resolve issues; • To oversee and participate in the tracking, troubleshooting, and resolution of issues logged internally and by clients; • To participate in the weekly product release and execute tests, analyze results of test runs, and report those results; • To comply with all internal regulations, procedures, and policies as per ISO and PCI DSS standards.