Lead Engineer

EPAM Systems Inc; Chennai; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

EPAM is seeking a Senior/Lead AI Engineer to architect, develop, and deploy enterprise AI solutions using LLMs, RAG, and Agentic AI frameworks.

You will lead multi-agent system designs, optimize AI workflows, and integrate AI capabilities with cloud platforms and enterprise applications. The role emphasizes governance, security, and scalable pipelines.

Qualifications

• 2+ years of hands-on experience building and deploying LLM, GenAI, or Agentic AI solutions in production.
• Strong experience designing and implementing multi-agent frameworks and orchestration systems.
• Experience with AI-assisted software development tools such as GitHub Copilot, Claude Code, Codex.
• Strong expertise in RAG architecture design and implementation.
• Experience optimizing AI models, prompts, workflows, and inference pipelines.
• Hands-on experience integrating AI applications with enterprise ecosystems and cloud services.

Responsibilities

• Design, build, and deploy enterprise-grade LLM, Generative AI, and Agentic AI solutions.
• Lead the architecture and implementation of multi-agent systems and complex AI workflows.
• Build and optimize RAG architectures for enterprise use cases.
• Fine-tune and improve AI/GenAI models for performance and cost efficiency.
• Integrate AI solutions with cloud platforms, APIs, and enterprise apps.
• Establish AI engineering best practices including governance and observability.

Skills

Python

LangChain

LangGraph

Tools

AgentBricks

AgentCore (AWS)

Microsoft Foundry

Gemini Agent Studio

Snowflake Cortex Agents

Job description

As a Senior / Lead AI Engineer, you will be responsible for architecting, developing, and deploying enterprise-scale AI solutions leveraging Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and Agentic AI frameworks. You will work closely with cross-functional teams to deliver innovative AI products while driving best practices in AI engineering, governance, and operational excellence.

Key Responsibilities

• Design, build, and deploy enterprise-grade LLM, Generative AI, and Agentic AI solutions.
• Lead the architecture and implementation of multi-agent systems and complex AI workflows.
• Build and optimize RAG (Retrieval Augmented Generation) architectures for enterprise use cases.
• Fine-tune, optimize, evaluate, and improve AI/GenAI models for performance, accuracy, scalability, and cost efficiency.
• Integrate AI solutions with cloud platforms, APIs, enterprise applications, and backend systems.
• Establish and maintain AI engineering best practices including AgentOps, governance, observability, security, and responsible AI principles.
• Implement automated deployment strategies using modern CI/CD pipelines.
• Collaborate with business stakeholders, architects, product teams, and engineering teams to ensure successful project delivery.
• Mentor and guide engineering teams on AI solution architecture and implementation.

Required Experience

• 2+ years of hands-on experience building and deploying LLM, GenAI, or Agentic AI solutions in production environments.
• Strong experience designing and implementing multi-agent frameworks and orchestration systems.
• Experience with AI-assisted software development tools such as:
  • GitHub Copilot
  • Claude Code
  • Codex
  • Similar AI SDLC tools
• Strong expertise in RAG architecture design and implementation.
• Experience optimizing AI models, prompts, workflows, and inference pipelines.
• Hands-on experience integrating AI applications with enterprise ecosystems and cloud services.

Technical Skills

Programming & Frameworks

• Python
• LangChain
• LangGraph

Enterprise AI Platforms & Frameworks

• AgentBricks
• AgentCore (AWS)
• Microsoft Foundry
• Gemini Agent Studio
• Snowflake Cortex Agents

Cloud & AI Platforms

• AWS Bedrock
• Databricks Mosaic AI
• Microsoft Foundry
• Gemini Enterprise Agent Platform
• Snowflake Cortex

DevOps & Engineering

• Docker
• Kubernetes
• CI/CD Pipelines
• AI Governance
• MLOps / AgentOps best practices

Preferred Qualifications

• Experience in enterprise-scale AI transformation programs.
• Knowledge of Responsible AI, AI governance frameworks, and compliance standards.
• Experience leading technical teams and driving AI solution strategy.
• Exposure to observability, monitoring, evaluation frameworks, and AI platform engineering.

Soft Skills

• Excellent verbal and written communication skills.
• Strong stakeholder management and collaboration abilities.
• Ability to lead cross-functional teams and deliver business outcomes.
• Problem-solving mindset with a focus on innovation and continuous improvement.

Why EPAM?

• Work on cutting-edge AI and GenAI initiatives for global enterprises.
• Collaborate with highly skilled architects, engineers, and AI specialists.
• Exposure to large-scale digital transformation and innovation programs.
• Continuous learning opportunities through EPAM's world-class engineering culture.
• Opportunity to shape the future of enterprise AI solutions
• Experience with retrieval augmented generation (RAG) algorithms for text summarization, question answering, etc.