Role Description Our client, a leading technology organization, is seeking a Senior AI Engineer to design and deliver scalable Generative AI solutions in a production environment. The role focuses on building enterprise-grade AI products using Python, React, and AWS while leveraging modern architectures including LLM pipelines, RAG, agentic systems, and MCP frameworks. The ideal candidate brings deep Gen AI expertise, strong cloud engineering fundamentals, and the ability to lead cross-functional delivery in an Agile environment. Key Responsibilities - Design, architect, and build scalable Gen AI solutions including LLM pipelines, Agentic AI, MCP, Graph/RAG architectures, and prompt-based applications - Lead end-to-end delivery of production-ready AI products using Python, React/Next.js, and AWS - Implement cloud-native solutions across AWS EKS, Lambda, Fargate, Glue, and Athena - Leverage Gen AI frameworks including LangChain, LlamaIndex, and Amazon Bedrock - Design and optimize vector and graph database solutions — Weaviate, Milvus, PGVector, Neo4J, Neptune - Build and orchestrate ETL pipelines using Apache Airflow/DAGs - Optimize AI product performance and drive continuous experimentation with cutting-edge Gen AI methods and toolchains - Serve as SME on Gen AI technologies and actively contribute to shaping the organization's AI roadmap - Own timelines, deliverables, and project milestones using Agile Scrum/Kanban practices - Monitor operational metrics and incident data to drive continuous improvement and reliability - Ensure adherence to DevSecOps protocols, governance standards, and SDLC best practices Qualifications - 7+ years of progressive engineering experience with 1–2 years leading AI or emerging tech initiatives - Hands-on expertise with Gen AI models — GPT, Claude, Gemini, LLaMA — and advanced prompt engineering - Strong experience with Agentic AI, MCP, and Graph/RAG architectures - Proficiency in Gen AI frameworks — LangChain, LlamaIndex, Amazon Bedrock - Web development proficiency in Next.js, React, and TypeScript/JavaScript - Deep AWS expertise — EC2, EKS, Fargate, Lambda, Athena, Glue, Lake Formation - Infrastructure as Code experience — Terraform, Docker, Puppet - Strong Python programming skills — NumPy, Pandas, Matplotlib, Boto3 - Vector and graph database experience — Weaviate, Milvus, PGVector, Neo4J, Neptune - ETL orchestration experience using Apache Airflow/DAGs - Experience with automated testing frameworks — Ragas, Playwright, Selenium, Zephyr - Familiarity with LLM fine-tuning techniques - Strong understanding of SDLC best practices, DevSecOps, and Agile Scrum/Kanban - Experience with JIRA, Confluence, and JIRA Align Nice-to-Have Skills - Experience with BI tools such as QuickSight or Tableau - Knowledge of financial markets and enterprise data systems - Background working in highly regulated or enterprise-scale environments

Role Description We are building a next-generation AI Payments Engineering Team to design and deliver an AI-native Payments Operating System. This team will combine AI engineering, payments domain expertise, and production automation to transform high-effort, high-risk financial operations into scalable, intelligent, and auditable systems. The team will focus on automating critical Payments processes such as: - Least cost routing - Reconciliations - Audit - Incident automation The mission is to deliver efficiency with control integrity—not just automation speed. Qualifications - AIOps Engineer - AI Automation Engineer - Senior AI Engineer (Payments & Architecture) Requirements - Design and deploy production-grade AI automation workflows that drive immediate operational impact. - Build and deploy agentic workflows for: - Reconciliations - Audit & reporting preparation Benefits - Experience-led everything - Commitment to creating an inclusive workplace - Equal employment opportunity (EEO) and affirmative action employer

• Build and deliver AI solutions: Take ownership of developing delightful, high-performance AI features to help users discover, organize, and optimize access to large datasets. • Rapid experimentation and iteration: Implement a highly iterative process where you quickly prototype, test, and validate with real users, including shipping and evolving LLM- or agent-powered workflows for the data engineering lifecycle. • Collaborate: Work with the rest of the team to shape AI-driven product features, including the integration of agentic components with internal tools like Slack and alerting systems while engaging with internal data teams for dogfooding. • Utilize AI tools effectively: Use AI and automation tools to enhance both product functionality and your own development workflows. • Effective communication: You’ll be working in a highly dynamic and collaborative environment, so we need someone who can communicate effectively and contribute across teams. • Ownership and impact: Take full ownership of the AI solutions you develop, ensuring they are not only innovative but also scalable, maintainable, and aligned with real user workflows.

• Build and deliver AI solutions: Take ownership of developing delightful, high-performance AI features to help users discover, organize, and optimize access to large datasets. • Rapid experimentation and iteration: Implement a highly iterative process where you quickly prototype, test, and validate with real users, including shipping and evolving LLM- or agent-powered workflows for the data engineering lifecycle. • Collaborate: Work with the rest of the team to shape AI-driven product features, including the integration of agentic components with internal tools like Slack and alerting systems while engaging with internal data teams for dogfooding. • Utilize AI tools effectively: Use AI and automation tools to enhance both product functionality and your own development workflows. • Effective communication: You’ll be working in a highly dynamic and collaborative environment, so we need someone who can communicate effectively and contribute across teams. • Ownership and impact: Take full ownership of the AI solutions you develop, ensuring they are not only innovative but also scalable, maintainable, and aligned with real user workflows.