Role Overview

• Lead the development and deployment of enterprise AI/ML solutions.

• Work closely with Microsoft / McKinsey technical and architecture teams.

• Translate architectural designs into scalable, production-ready systems.

• Build LLM and Agentic AI applications.

• Technically validate and challenge architecture decisions.

• Lead engineers while remaining hands-on with coding and implementation.

• Ensure strong engineering practices, quality, security, scalability, and reliability.

Key Responsibilities

• Lead AI/ML solution build, deployment, and implementation.

• Supervise and mentor the engineering team.

• Review and validate architecture proposed by external partners/architects.

• Translate architecture and design patterns into production implementations.

• Build reusable frameworks, core components, and reference implementations.

• Own code quality, testing, engineering standards, and MLOps.

• Conduct code reviews and establish best practices.

• Implement Git, CI/CD, secure coding, testing, and evaluation practices.

• Identify technical risks, dependencies, and design gaps.

• Lead development of:

  • LLM applications

  • RAG solutions

  • AI Agents / Agentic AI

  • LLM orchestration

  • Tool/function calling

  • LLM evaluation frameworks

• Provide technical guidance and mentorship.

• Support knowledge transfer to client/AM teams.

• Ensure solutions are production-ready, scalable, secure, reliable, and maintainable.

Required Skills & Experience

• 8–11 years of experience in Software Engineering, AI Engineering, or ML Engineering.

• Strong recent hands-on coding experience.

• Strong Python proficiency.

• Strong experience with TensorFlow and/or PyTorch.

• Proven experience building and deploying LLM / Generative AI / Agentic AI solutions in production.

• Hands-on experience with:

  • RAG

  • LLM Orchestration

  • AI Agents / Agentic Workflows

  • Tool / Function Calling

  • Prompt Engineering

  • LLM Evaluation

• Strong software engineering fundamentals:

  • Clean code

  • Unit & integration testing

  • Git / Version Control

  • CI/CD

  • Secure coding

  • Code reviews

  • MLOps

• Experience leading or supervising engineering teams.

• Ability to evaluate and technically challenge architecture.

• Strong problem-solving and stakeholder management skills.

• Excellent communication skills.

• Ability to take end-to-end ownership.

Good to Have

• Azure AI Foundry

• Azure OpenAI

• LangChain

• LangGraph

• Semantic Kernel

• AutoGen

• Microsoft Azure / Cloud AI platforms

• Docker / Kubernetes

• Enterprise or manufacturing experience

• Partner/vendor-led architecture experience

• Compliance, security, governance, or regulated-environment exposure

• Experience mentoring client teams

Mandatory Technical Skills

• Python

• TensorFlow / PyTorch

• Generative AI / LLM

• RAG

• AI Agents / Agentic AI

• LLM Orchestration

• Tool / Function Calling

• LLM Evaluation

• Software Engineering

• CI/CD

• MLOps

• Git / Version Control

Ideal Candidate

The ideal candidate should be a hands-on AI/ML Technical Lead who can:

• Build production-grade AI solutions.

• Lead and mentor engineering teams.

• Challenge and validate complex technical architectures.

• Bridge the gap between architecture and implementation.

• Establish strong engineering and MLOps practices.

• Work effectively with architects, clients, partners, and engineering teams.

• Take complete ownership of technical delivery.