EPAM Systems
Senior Software Engineer - Python with GenAI, LLM
Build production-grade Generative AI solutions using LLMs, agentic workflows, and RAG architectures with Python and cloud AI platforms.
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 5–7 years of professional experience in software development
- Hands-on experience building applications using Generative AI and LLM technologies
- Strong proficiency in Python and experience developing production-ready applications
- Hands-on experience with at least two agentic AI frameworks such as LangChain, LangGraph, Google ADK, CrewAI, AutoGen, or Microsoft Copilot extensibility
- Experience with cloud AI platforms including Azure OpenAI, AWS Bedrock, or Google Vertex AI/ADK
- Strong backend development skills, including REST/gRPC APIs, asynchronous programming, Docker, and frameworks such as FastAPI or Flask
- Solid understanding of leading LLMs including OpenAI GPT models, Anthropic Claude, Google Gemini, and open-source alternatives
- Practical experience building RAG solutions using vector databases such as Pinecone, Weaviate, ChromaDB, or Qdrant
- Expertise in prompt engineering, LLM orchestration, structured outputs, guardrails, ReAct patterns, and evaluation techniques
- Strong problem-solving, system design, and architectural decision-making skills
- Excellent communication skills with the ability to collaborate effectively across global teams
Full description
We are seeking an experienced AI Engineer to design and build production-grade Generative AI solutions with a strong focus on agentic workflows, multi-agent systems, and enterprise AI applications. The ideal candidate combines strong software engineering fundamentals with hands-on expertise in LLMs, RAG architectures, and AI agent frameworks.
Responsibilities
- Design, architect, and develop scalable Generative AI applications and agentic solutions for real-world business use cases
- Build and orchestrate AI agents using frameworks such as LangChain, LangGraph, Google ADK, CrewAI, AutoGen, Microsoft Copilot Studio, or similar technologies
- Develop and maintain backend services, APIs, microservices, and data pipelines that power AI-driven products
- Implement advanced AI patterns including RAG, Agentic RAG, tool/function calling, planning & reflection loops, and human-in-the-loop workflows
- Engineer and optimize prompts, system instructions, and agent workflows to improve reliability, accuracy, and user experience
- Integrate LLMs with enterprise systems, third-party APIs, vector databases, and knowledge repositories
- Monitor, evaluate, and continuously improve model and agent performance using observability tools, metrics, and user feedback
- Collaborate closely with Product, Engineering, Data, and Design teams to deliver impactful AI solutions
- Stay current with emerging AI technologies, frameworks, and best practices, contributing innovative ideas to the team
- Document architectures, design decisions, and reusable solution patterns while supporting knowledge sharing across teams
Requirements
- 5–7 years of professional experience in software development
- Hands-on experience building applications using Generative AI and LLM technologies
- Strong proficiency in Python and experience developing production-ready applications
- Hands-on experience with at least two agentic AI frameworks such as LangChain, LangGraph, Google ADK, CrewAI, AutoGen, or Microsoft Copilot extensibility
- Experience with cloud AI platforms including Azure OpenAI, AWS Bedrock, or Google Vertex AI/ADK
- Strong backend development skills, including REST/gRPC APIs, asynchronous programming, Docker, and frameworks such as FastAPI or Flask
- Solid understanding of leading LLMs including OpenAI GPT models, Anthropic Claude, Google Gemini, and open-source alternatives
- Practical experience building RAG solutions using vector databases such as Pinecone, Weaviate, ChromaDB, or Qdrant
- Expertise in prompt engineering, LLM orchestration, structured outputs, guardrails, ReAct patterns, and evaluation techniques
- Strong problem-solving, system design, and architectural decision-making skills