Technical Lead

SourcingXPress; Maharashtra; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Tenarai in India is seeking an experienced Tech Lead to drive the development and deployment of enterprise AI/ML solutions. You will collaborate with Microsoft and McKinsey technical teams to translate designs into scalable, production-ready systems.

You will lead engineers while coding hands-on, ensuring best practices, security, scalability, and reliable delivery of LLM/Agentic AI projects, RAG solutions, and orchestration across teams.

Qualifications

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

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, LLM orchestration, tool/function calling, LLM evaluation frameworks.

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

Tools

Docker / Kubernetes

LangChain

LangGraph

Semantic Kernel

AutoGen

Job description

• Lead the development and deployment of enterprise AI/ML solutions.
• Work closely with Microsoft / McKinsey technical and architecture teams.
• 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.

Company: Tenarai

Website: Visit Website

LinkedIn: Visit LinkedIn

Business Type: Enterprise

Business Type: Enterprise

Business Model: B2B

Funding Stage: Series A

Industry: Information Technology

Salary Range: ₹ 25-45 Lacs PA

Job Description

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.