Senior Software Engineer (Python

EPAM Systems; Coimbatore District; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

EPAM Systems in India seeks an experienced AI Engineer to design and build production-grade Generative AI solutions with a focus on agentic workflows and enterprise AI applications.

You will work with LangChain, LangGraph, and other frameworks to orchestrate AI agents, develop backend services, and integrate with cloud AI platforms. Collaboration with Product, Engineering, Data, and Design teams will be essential to deliver impactful AI products.

Qualifications

• 5–7 years of software development experience.
• Hands-on with Generative AI and LLM tech.
• Proficient in Python for production apps.
• Experience with multiple agent frameworks (LangChain, LangGraph, etc.).
• Experience with cloud AI platforms (Azure, AWS, Google).
• Backend skills: REST/gRPC, async, Docker, FastAPI/Flask.
• Strong communication and collaboration across teams.

Responsibilities

• Design, architect, and develop scalable Generative AI apps and agentic solutions.
• Build AI agents using frameworks like LangChain, LangGraph, and more.
• Develop backend services, APIs, and data pipelines powering AI products.
• Implement RAG, agentic patterns, tool calling, and human-in-the-loop workflows.
• Engineer prompts and agent workflows to improve reliability and UX.
• Integrate LLMs with enterprise systems and external APIs.
• Monitor model/agent performance with observability tools and feedback.
• Collaborate with Product, Engineering, Data, and Design teams.

Skills

Python

LLMs

System design

Problem solving

Communication

Architecture decisions

Tools

LangChain

LangGraph

Google ADK

CrewAI

AutoGen

Copilot Studio

Docker

FastAPI/Flask

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 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