AI/ML Lead

Job Description

AI/ML Engineering Lead GenAI & Agentic AI

Experience: 8- 12 Years
Location: Indore
Work Mode: Work From Office
Employment: Full Time

Role Overview

We are looking for a hands-on AI/ML Engineering Lead to architect and lead an internal GenAI/Agentic AI project while leading and mentoring a team of fresh AI/ML engineers.

This is not a trainer role. The Lead will mentor engineers through live project development, own the technical architecture and delivery, and progressively build the team into client-deployable AI engineers within ~6 months.

The role is also responsible for cascading our AI-first vision and establishing strong AI engineering practices across the team.

Key Responsibilities

• Architect, design and lead development of internal GenAI/Agentic AI products.
• Lead and mentor a team of fresh/junior AI/ML engineers through real project execution.
• Break the project into modules, assign ownership and drive delivery.
• Conduct architecture, code and design reviews.
• Remain hands-on with critical and complex technical components.
• Build production-grade solutions using Python, LLMs, RAG, LangChain, LangGraph, MCP and AI Agents.
• Guide engineers on software engineering, testing, APIs, deployment and cloud practices.
• Establish coding, architecture, security, evaluation and deployment standards.
• Identify skill gaps and progressively increase engineers' technical ownership.
• Ensure engineers become capable of independently contributing to enterprise/client AI projects within approximately 6 months.
• Evaluate emerging AI technologies and incorporate relevant capabilities into internal projects.
• Cascade and implement the organization's AI-first vision within the engineering team.
• Collaborate with technology, delivery and business teams to align internal AI capabilities with client requirements.

Must-Have Technical Skills

Strong Python

• Strong hands-on Python engineering.
• REST APIs / FastAPI / Flask.
• Good software engineering, OOP and debugging skills.

GenAI / LLM

• LLM application development
• RAG
• Embeddings & Vector Databases
• Prompt Engineering
• Function/Tool Calling
• Structured Outputs
• LLM evaluation

Agentic AI

• LangChain
• LangGraph
• MCP (Model Context Protocol)
• AI Agents / Agent orchestration
• Multi-step workflows
• Agent memory/context
• Tool integration

Production & Deployment

• Docker
• Git / CI/CD
• AWS / Azure / GCP
• API & microservice development
• Production deployment
• Logging, monitoring and observability

Good to Have

• AWS Bedrock / Azure OpenAI / OpenAI APIs
• Pinecone / Qdrant / Weaviate / Milvus / pgvector
• PostgreSQL / Redis
• Kubernetes
• LLM observability & evaluation
• AI security / guardrails
• Multimodal AI

Leadership Expectations

The ideal candidate is:

• Hands-on technical leader, not a trainer or people manager only
• Strong in AI architecture and technical decision-making
• Comfortable coding and reviewing production code
• Experienced in mentoring junior engineers through actual project delivery
• Strong problem solver with excellent debugging skills
• Able to convert business requirements into scalable AI solutions
• Passionate about emerging GenAI and Agentic AI technologies
• Strong communicator who can build an AI-first engineering culture

Success Criteria

Within approximately 6 months, the Lead should be able to:

Architect the project Lead development Mentor fresh engineers Deliver production-oriented AI solutions Build independent engineering capability Make suitable engineers client-deployable.

Ideal Candidate

We are looking for someone who can:

Architect it. Build it. Lead it. Mentor it. Deploy it.

Candidates with experience limited to traditional ML, academic research, prompt engineering, basic LLM API integrations or classroom training are not the target profile.

Work From Office is mandatory.