Job description

We are looking for a Lead Python AI Engineer to build robust, AI-powered API services and agentic workflows. In this role, you will bridge the gap between traditional backend systems and modern language models, focusing on secure and scalable LLM interactions with internal systems, vector databases, and external tools via orchestration frameworks. We treat LLMs not as magic, but as components in a secure, scalable distributed system. If you take pride in API reliability, graceful failure handling, and production-grade AI integration, we’d love to talk to you. Req# 1109260679 Responsibilities: • End-to-End Ownership: Own API contracts and service reliability end-to-end. AI edge cases must fail gracefully without compromising system integrity • Rigorous Systems Engineering: Integrate LLMs with a strong engineering foundation—baking in evaluation, comprehensive observability, robust fallback handling, and cost awareness • Reusable Frameworks: Build reliable, reusable orchestration frameworks and logic designed for downstream developer consumption • Design and build scalable backend API services and advanced LLM orchestration layers • Implement and maintain complex RAG (Retrieval-Augmented Generation) pipelines, covering document ingestion, chunking, embedding, and retrieval optimization • Develop and connect agent tools using LangChain, LangGraph, and potentially MCP (Model Context Protocol) • Ensure top-tier security, privacy, enterprise-grade observability, and comprehensive test coverage across all backend workflows • Contribute actively to architecture decisions and engineering standards within your pod • Collaborate cross-functionally with frontend engineers, data engineers, and infrastructure teams Requirements: • Backend Experience: 7+ years of backend engineering with a heavy focus on microservices and distributed systems, using Python for high-performance backend services and cloud-native APIs • Cloud & Infrastructure: Hands-on experience with AWS, Docker, ECS/EKS, and RESTful APIs • Security First: Proven experience with secure coding practices and strong authentication/authorization (auth/authz) fundamentals • Agentic Frameworks: Practical, hands-on experience with frameworks such as LangChain, LlamaIndex, or LangGraph • AI SDK Integration: 1–2+ years of hands-on experience integrating AI SDKs (OpenAI, Anthropic/Claude, AWS Bedrock, or equivalent) in production environments • AI Observability & Evaluation: Familiarity with AI evaluation tooling or real-time APM platforms (e.g., LangSmith, Langfuse, Arize) • RAG Pipelines: Practical experience working with vector databases (e.g., Amazon Kendra, OpenSearch), advanced embedding strategies, and retrieval systems Nice to have: • Frontend Fluency: 1–2+ years of experience with React and TypeScript • Scale & Deployment: Experience deploying applications to EKS environments at scale • Interoperability & Security: Knowledge of agent interoperability patterns (MCP) and identity/security domain models (IAM, CIAM) • Secondary Languages: Professional proficiency in Java, Node.js, or Go

Requirements

Must-haves

We treat LLMs not as magic, but as components in a secure, scalable distributed system. If you take pride in API reliability, graceful failure handling, and production-grade AI integration, we’d love to talk to you.
Req# 1109260679
End-to-End Ownership: Own API contracts and service reliability end-to-end. AI edge cases must fail gracefully without compromising system integrity
Rigorous Systems Engineering: Integrate LLMs with a strong engineering foundation—baking in evaluation, comprehensive observability, robust fallback handling, and cost awareness
Reusable Frameworks: Build reliable, reusable orchestration frameworks and logic designed for downstream developer consumption
Design and build scalable backend API services and advanced LLM orchestration layers
Implement and maintain complex RAG (Retrieval-Augmented Generation) pipelines, covering document ingestion, chunking, embedding, and retrieval optimization
Develop and connect agent tools using LangChain, LangGraph, and potentially MCP (Model Context Protocol)
Ensure top-tier security, privacy, enterprise-grade observability, and comprehensive test coverage across all backend workflows
Contribute actively to architecture decisions and engineering standards within your pod
Collaborate cross-functionally with frontend engineers, data engineers, and infrastructure teams
Backend Experience: 7+ years of backend engineering with a heavy focus on microservices and distributed systems, using Python for high-performance backend services and cloud-native APIs
Cloud & Infrastructure: Hands-on experience with AWS, Docker, ECS/EKS, and RESTful APIs
Security First: Proven experience with secure coding practices and strong authentication/authorization (auth/authz) fundamentals
Agentic Frameworks: Practical, hands-on experience with frameworks such as LangChain, LlamaIndex, or LangGraph
AI SDK Integration: 1–2+ years of hands-on experience integrating AI SDKs (OpenAI, Anthropic/Claude, AWS Bedrock, or equivalent) in production environments
AI Observability & Evaluation: Familiarity with AI evaluation tooling or real-time APM platforms (e.g., LangSmith, Langfuse, Arize)
RAG Pipelines: Practical experience working with vector databases (e.g., Amazon Kendra, OpenSearch), advanced embedding strategies, and retrieval systems
Frontend Fluency: 1–2+ years of experience with React and TypeScript

Nice-to-haves

Scale & Deployment: Experience deploying applications to EKS environments at scale
Interoperability & Security: Knowledge of agent interoperability patterns (MCP) and identity/security domain models (IAM, CIAM)
Secondary Languages: Professional proficiency in Java, Node.js, or Go

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

pythonaiapillmawsdockerreacttypescriptjavanode.jsgo

Platform: epam
Effort: medium