AI Software Engineer

Compensation: INR 20-35 LPA Base + Bonus + Equity

Location: Gurgaon (Full time In-office)

What you need

• 2+ years shipping production backend code
• Strong Python and SQL
• Real experience integrating APIs across systems you do not control
• GCP (or AWS/Azure) and Docker
• Working knowledge of LLM systems: prompting, evals, retrieval, tool calling, agents
• Excellent spoken English. You will be on calls with US physicians weekly
• Comfort with ambiguity. Nobody hands you a spec

Bonus: Voice/telephony (LiveKit, Twilio, ElevenLabs), EHR or RCM exposure (eClinicalWorks, Athena, HL7, FHIR), prior customer-facing engineering, fluency with Claude Code or Cursor.

About Us

We build the AI operating system for specialty medical practices in the US. Our voice agents answer patient calls, schedule appointments, chase prior authorizations and win back lapsed patients, live today across gastroenterology, nephrology, dermatology and radiology.

Founded by AI/ML engineers from Google, Amazon and Snowflake. Small team, well funded, moving fast.

The role

An AI Engineer ships production software inside a customer's reality. You take a live medical practice with its own EHR, its own scheduling rules and its own five exceptions to every rule, and you build the agent that works there.

Half your week is code. The other half is calls with practice administrators figuring out what the code needs to do. You own accounts end to end and your name is on whether they succeed.This is not support or implementation. It is engineering with the customer in the room.

You will:

• Own deployments from scoping to go-live for named US accounts
• Build integrations against EHRs, scheduling platforms and telephony
• Debug the full path: voice, LLM orchestration, EHR write-back
• Own agent performance in production. Read transcripts, find failures, fix them, prove it with data
• Turn one-off customer work into reusable tooling

Why here

• You hear your work. Listen to a real patient booking a colonoscopy with an agent you shipped last month.
• You write the playbook. The FDE team is small. Early hires define how we deploy, and the strongest lead pods within a year.
• No layers. Direct line to the founders, because you hold the field data.