Lead Software Engineer (Python, GenAI, LLM)

EPAM Systems; Bengaluru; On-site; INR 3,800,000 - 6,000,000; Full time

Job summary

EPAM Systems in Bengaluru is seeking an experienced AI Engineer to design and build production-grade Generative AI solutions with a strong focus on agentic workflows and multi-agent systems for enterprise applications.

The candidate will combine solid software engineering with hands-on expertise in LLMs, RAG architectures, and AI agent frameworks, collaborating across product and engineering teams to deliver impactful AI products.

Qualifications

• 7 to 12 years of relevant professional experience.
• Hands-on experience building Generative AI and LLM applications.
• Strong Python programming for production-ready software.

Responsibilities

• Design, architect, and develop scalable Generative AI applications and agentic solutions for real-world business use cases.
• Build and orchestrate AI agents using multiple frameworks.
• Develop and maintain backend services, APIs, microservices, and data pipelines powering 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 and UX.
• Integrate LLMs with enterprise systems, third-party APIs, vector databases, and knowledge repositories.
• Monitor, evaluate, and improve model and agent performance using observability tools and metrics.
• Collaborate with Product, Engineering, Data, and Design teams to deliver AI solutions.
• Stay current with emerging AI technologies and best practices.

Tools

LangChain

LangGraph

Google ADK

CrewAI

AutoGen

Microsoft Copilot Studio

REST APIs

gRPC

Docker

FastAPI

Flask

Azure OpenAI

AWS Bedrock

Google Vertex AI/ADK

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

• 7 to 12 years of relevant professional experience


• 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