Senior Software Engineer - Python with GenAI, LLM

EPAM Systems; India; On-site; INR 1,500,000 - 2,800,000; Full time

Job summary

EPAM Systems is seeking an experienced AI Engineer to design production-grade Generative AI solutions with a strong focus on agentic workflows, multi-agent systems, and enterprise AI applications. You will architect scalable applications and collaborate across product, engineering, and design teams.

Bring 5–7 years of software experience, hands-on AI/LLM work, Python excellence, and experience with agent frameworks and cloud AI platforms.

Qualifications

• 5–7 years of software engineering experience.
• Hands-on with Generative AI and LLM technologies.
• Strong Python skills and production-grade application development.
• Experience with two or more agentic AI frameworks (e.g., LangChain, LangGraph, Google ADK, CrewAI, AutoGen, or Copilot extensibility).
• Experience with cloud AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI/ADK).
• Backend development: REST/gRPC APIs, asynchronous programming, Docker, and frameworks such as FastAPI or Flask.
• Familiarity with vector databases for RAG solutions (e.g., Pinecone, Weaviate, ChromaDB, Qdrant).
• Prominent expertise in prompt engineering, LLM orchestration, and evaluation techniques.
• Strong problem solving and system design and architectural decision-making skills.
• Excellent communication and cross-team collaboration across global teams.

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.

Skills

Software engineering

Generative AI

Python

Agent frameworks

Cloud platforms

LLMs

APIs & Microservices

Prompt engineering

System design

Collaboration

Tools

LangChain

LangGraph

Google ADK

CrewAI

AutoGen

Copilot Studio

REST/ gRPC

Docker

FastAPI

Flask

Pinecone

Weaviate

ChromaDB

Qdrant

Job 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 experience in software engineering
• 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