Senior Software Engineer,

Epam Systems; Gurugram District; On-site; INR 2,500,000 - 4,200,000; Full time

Job summary

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

The role combines strong software engineering fundamentals with hands-on expertise in LLMs, RAG architectures, and AI agent frameworks. You will design scalable AI solutions, integrate with enterprise systems, and collaborate across Product, Engineering, Data, and Design teams.

Qualifications

• 712 years of overall software engineering experience.
• 3+ years hands-on experience building applications using Generative AI and LLM technologies.
• Strong proficiency in Python and production-ready applications.
• Hands-on experience with at least two agentic AI frameworks (LangChain, LangGraph, Google ADK, CrewAI, AutoGen, Microsoft Copilot extensibility).
• Strong backend development skills including REST/gRPC APIs, asynchronous programming, Docker, and FastAPI/Flask.
• Solid understanding of leading LLMs (OpenAI GPT, Claude, Gemini, etc.).
• Experience building RAG solutions with vector databases (Pinecone, Weaviate, ChromaDB, Qdrant).
• Expertise in prompt engineering, LLM orchestration, guardrails, ReAct, evaluation techniques.
• Strong problem-solving, system design and architectural decision-making.
• Excellent communication across global teams.

Responsibilities

• Design, architect, and develop scalable Generative AI apps and agentic solutions.
• Build and orchestrate AI agents using LangChain, LangGraph, and other frameworks.
• Develop backend services, APIs, microservices, and data pipelines for AI products.
• Implement RAG, Agentic RAG, tool calls, planning & reflection loops, human-in-the-loop workflows.
• Engineer prompts, system instructions, and agent workflows for reliability and UX.
• Integrate LLMs with enterprise systems and external APIs, vector stores, knowledge bases.
• Monitor and improve model/agent performance with observability tools and feedback.
• Collaborate with Product, Engineering, Data and Design teams.
• Stay current with AI frameworks and technologies, share knowledge across teams.
• Document architectures and reusable patterns for knowledge sharing.

Skills

Software engineering experience (712+?

Generative AI & LLMs

Python proficiency

Agentic AI frameworks

Backend development (REST/gRPC, Docker

LLMs (GPT, Claude, Gemini)

RAG with vector DBs

Prompt engineering & orchestration

Problem solving & system design

Cross-team communication

Tools

LangChain

LangGraph

Google ADK

CrewAI

AutoGen

Microsoft Copilot Studio

Job description

Role Overview

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.

Key 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.

Required Qualifications

Must Have

• 712 years of overall software engineering experience.
• 3+ years of 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.
• 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.

Preferred Qualifications

• Experience with AI observability and evaluation tools such as LangSmith, Ragas, DeepEval, Arize, or Weights & Biases.
• Familiarity with model adaptation and fine‑tuning techniques (LoRA, PEFT, RLHF concepts).
• Experience with cloud AI platforms including Azure OpenAI, AWS Bedrock, or Google Vertex AI.
• Understanding of CI/CD, MLOps, and LLMOps practices.
• Exposure to graph databases, knowledge graphs, and structured data integration.
• Experience with event‑driven architectures and messaging platforms such as Kafka or RabbitMQ.

What We're Looking For

• Engineers who build and deliver production‑ready AI systems, not just prototypes.
• Professionals who can evaluate and recommend the right architecture, whether single‑agent, multi‑agent, RAG‑based, or fine‑tuned solutions.
• Strong ownership mindset with the ability to take AI solutions from concept through production deployment.
• Team players who contribute technical leadership, best practices, and continuous learning within a rapidly evolving AI landscape.