Machine Learning Engineer (Tech Lead) – GenAI & Applied AI

About the Role

We are looking for a hands-on Machine Learning Engineer (MLE) Tech Lead with 7+ years of experience who has built, deployed, and scaled ML/GenAI solutions in real production environments .

This is not a pure Data Scientist role . Please read on the Job Description to check if you are the right fit.

We are looking for someone who understands how ML models live inside backend services, applications, and platforms , and who can lead technically while remaining deeply hands-on.

What Youll Do

• Design, build, and deploy ML / GenAI solutions end-to-end (from model to production)
• Own production ML systems , including APIs, inference services, and monitoring
• Work closely with backend and frontend engineers to integrate ML into applications
• Build and optimize RAG pipelines, LLM-based services, and agentic workflows
• Make trade-offs between accuracy, latency, cost, and scalability
• Implement MLOps best practices (CI/CD, versioning, rollback, monitoring)
• Act as a technical lead / player-coach , guiding engineers on ML system design
• Collaborate with product and business stakeholders to translate requirements into solutions

Required Experience & Skills

• 7+ years of experience in ML Engineering / Applied AI
• Strong hands-on experience with Python and ML frameworks
• Proven experience deploying ML models or GenAI systems into production
• Experience building REST APIs (FastAPI, Flask, Node.js, etc.)
• Solid understanding of cloud platforms (AWS, Azure, or GCP)
• Experience with Docker, Kubernetes, CI/CD pipelines
• Hands-on exposure to:
• LLMs & GenAI (OpenAI, LLaMA, Claude, etc.)
• RAG architectures (vector databases, embeddings, chunking strategies)
• Ability to work across ML + application engineering boundaries

Nice to Have

• Experience with agentic AI frameworks (LangChain, LangGraph, AutoGen, CrewAI)
• Experience with vector databases (Pinecone, FAISS, Chroma, etc.)
• Prior experience leading or mentoring engineers
• Exposure to monitoring ML systems (drift, latency, cost, reliability)

What This Role Is NOT

• Not a research-only role
• Not a notebook-only / experimentation-only role
• Not a pure Data Scientist position

This role is for someone who ships ML into real systems .

Why Join Us

• Work on real-world AI and GenAI problems , not just POCs
• High ownership and technical influence
• Opportunity to shape how AI systems are built and deployed
• Collaborative, engineering-first culture with minimal bureaucracy
• And we'll definitely pay well

How to Apply

If you enjoy building production-grade ML systems , working closely with engineers, and leading through hands-on execution, wed love to hear from you.

Apply via LinkedIn or apply directly in our website.