AI Platform Engineer / ML Infrastructure Engineer

US / India Deep-Tech Startup | Chennai & San Jose

(Full-time

• On-site / Hybrid)

About Us

We are a US / India deep-tech startup based in San Jose, CA and Chennai , building next-generation AI-driven medical intelligence systems .

Our work sits at the intersection of :

We operate in a domain where reliability, interpretability, and intelligent automation truly matter.

If you enjoy working on technically challenging problems that have real-world impact, you’ll feel at home here.

Role Overview

We are looking for an engineer who can design end-to-end AI and data platforms —from data ingestion to inference—across vector search, graph-based modeling, ML infrastructure, and cloud-native deployment.

This role blends modern ML Ops, data engineering, knowledge retrieval, and systems design.

If you are comfortable thinking across multiple layers of the stack and enjoy solving open-ended problems, we’d love to speak with you.

What You’ll Work OnData & Retrieval Systems

ML & Inference Infrastructure

Cloud & Infra Automation

Systems-Level Problem Solving

What We’re Looking For

We don’t expect you to know everything — but you should be strong in some of these areas and curious about the rest :

Core Skills

Bonus Skills (not required but appreciated)

Who You Are

Location

Chennai preferred, hybrid options available.

Qualifications & Experience Required Qualifications

We are looking for engineers with strong foundational experience across data systems, ML infrastructure, or cloud-native platforms. You should have :

Preferred Experience (Good to Have, Not Mandatory)

These are not hard requirements — but if you have them, we’d love to hear about it :

Experience Range

We expect candidates to have a meaningful track record building or deploying technical systems.

Typically this means 4–10 years of experience , depending on depth and exposure.

Why Join Us

You will work on deeply meaningful problems at the frontier of AI, safety, and intelligent automation.

This is not a typical “ML model training” role — it’s a platform engineering role for someone who likes to architect, design, and build real systems end-to-end.