Role Overview

We want our clinicians to spend their time on what only they can do: caring for people. The mundane, repetitive work around that care, we want AI to take off their plate. In this role, you'll go find those problems - the manual, time-consuming, easy-to-get-wrong tasks - and build AI systems that solve them at scale and securely. You'll own ideas end to end, from spotting the problem to shipping something people actually use.

What You'll Do
Find the repetitive, manual work that pulls clinicians and care teams away from people, and build AI that takes it off their plate
Turn ambiguous care and operations problems into working prototypes, automations, and agents - and ship them
Design every system for scale and security from day one; with sensitive work, safety and privacy come first, not last
Use LLMs, RAG, agents, MCP, and tool-calling to automate real workflows end to end
Take an idea to a deployed, working product quickly, then improve it based on what's actually live
Use AI tools across your own workflow - writing, reviewing, debugging, shipping
Work closely with clinical, product, and ops teams to understand the real problem before you build

What We're Looking For
Hands-on experience building with LLMs - RAG, agents,



tool-calling, automations - not just following tutorials
Enough range across the stack to take an idea to a deployed product on your own (Python and/or JavaScript, APIs, some cloud)
Robust fundamentals - you understand how the systems you use actually work, not just how to call them
A security- and privacy-first instinct, especially with sensitive healthcare data, and familiarity with HIPAA and PHI
You already use AI tools across your daily workflow
You can show the work - repos, prototypes, live links
A builder's bias - you'd rather ship a rough working version and improve it than plan forever
A explicit communicator who can turn a fuzzy problem into a concrete build

Nice to Have
Experience running agentic systems or workflow automations in production
Familiarity with vector databases, embeddings, and retrieval pipelines
Comfort with cloud infrastructure (GCP or AWS), containers, and CI/CD
An eye for evaluation - you check whether your AI actually works, not just that it runs
Interest in healthcare, mental health, or building for real-world impact
Experience in a rapid-paced product startup

📌 Ai Engineer Mumbai
🏢 Amaha
📍 Mumbai