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 clear 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 fast-paced product startup

📌 AI Engineer (Mumbai)
🏢 Amaha
📍 Mumbai