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# Lead Python AI Engineer

- **Company:** EPAM Systems, Inc.
- **Location:** Austin, TX, United States (Remote available)
- **Experience:** Expert
- **Salary:** $130,000.0 - $160,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, AI Evaluation, Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Application Frameworks, Systems Engineering, Automated Storage and Retrieval Systems, Cloud Computing, Cloud Engineering, Code Coverage, Encodings, Distributed Systems, Identity and Access Management, Interoperability, Python (Programming Language), Language Modeling, Node.Js, OpenAI, Secure Coding, Software Deployment, Systems Integration, TypeScript, Web Services, ReactJS, Retrieval-Augmented Generation, Large Language Models, Llamaindex, Backend, Langfuse — LLM Observability and Analytics Platform, Agentic-AI, Customer Identity Access Management, Build Management, LangSmith, Kubernetes, Machine Learning Operations, Front End Software Development, Claude, Restful APIs, Model Context Protocol, Api Management, OpenSearch, Docker, Microservices
- **Published:** October 18, 2026
- **Apply:** https://dejobs.org/x/x/07C75086159548AAB7BFD7BB5AF8F8DD/job/

## About the Role

* 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

## 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., * 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