Sr. AI/ML Engineer

at CombineHealth

Sr. AI/ML Engineer

CombineHealth

Company

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Sr. AI/ML Engineer

at CombineHealth

Posted by Raghu Venkatram

2 - 4 yrs

₹30L - ₹50L / yr

Bengaluru (Bangalore)

Skills

Large Language Models (LLM)

Artificial Intelligence (AI)

About CombineHealth

CombineHealth builds AI employees for healthcare revenue cycle management — agents that automate medical coding, billing, eligibility checks, denial management, and appeals for hospitals and physician groups across the US. Our platform runs in production at 97%+ coding accuracy across live customers, integrated with Epic, Cerner, athenahealth, and other major EHRs.

Before You Apply — Read This First

4 days/week in our HSR Layout, Bangalore office. Remote or fully-hybrid-light setups won't work for this team.

Regular overlap with US business hours. Our customers, most of our leadership, and key stakeholders are US-based. You'll need flexibility for calls and collaboration that land in US morning/afternoon (India evening/night) on a recurring basis, not occasionally.

If either of these is a dealbreaker, save yourself and us the time — this isn't the role.

What You'll Actually Do

This is an agent-engineering role. Our systems are LLM-based agents, and the hard part isn't training models — it's making autonomous, multi-step systems reliable and auditable on regulated, high-stakes data. You'll build the agents and own getting them into production reliably. Roughly:

Build AI agents that read unstructured clinical and claims documentation and produce structured, auditable outputs — codes, claim decisions, denial root causes, appeal drafts
Design the agent architecture: multi-step orchestration, tool/function calling, retrieval, and context engineering — plus the prompt and evaluation loop that makes it reliable, not just good in a demo
Build eval harnesses and regression suites with hard accuracy bars. "The eval moved" is the unit of progress here — if you can't measure whether a change helped, it didn't happen
Own reliability and observability in production: tracing, logging, latency, cost, failure handling — and yes, the pager when your agent breaks at 11pm US time
Design confidence-threshold and human-in-the-loop review systems — this is regulated, high-stakes data; "mostly right" isn't good enough, and every decision needs a traceable rationale
Work directly with product and RCM domain experts to translate messy real-world payer behavior into agent requirements
Own your systems end-to-end: if it breaks in production, you're the one who gets paged, not someone else

What We Need

5+ years shipping production software, with real depth building LLM-based systems — agents, RAG, structured extraction. Production systems with real users and real data, not POCs or research. Tenure in classical ML or CV is not what we're screening for.
Strong engineering fundamentals. You write production-grade Python, not notebooks. You design systems, handle failure paths, and care about latency and cost.
Fluency in the LLM application stack: prompt and context engineering, tool/function calling, retrieval, and — most important — eval-driven development. You know how to measure whether a change actually improved the system.
Comfortable owning your system in production end-to-end: deployment, observability, tracing, debugging live issues, cloud (AWS/GCP/Azure). We're not expecting a dedicated infra specialist, but you own your agent in production — you don't throw it over a wall.
Experience with unstructured/messy real-world text where the output has to be accurate and explainable. Healthcare is a bonus, not a requirement — legal, financial services, insurance, or other high-stakes-accuracy domains count.
Demonstrated ability to work independently and make judgment calls. We will not micromanage you, and we need people who don't need it.
Comfortable in a fast-moving startup environment: ambiguity, shifting priorities, and building things that don't have a textbook answer yet.
Degree from a Tier-1 engineering institute (IIT/IISc or equivalent) is a plus, but we care far more about what you've shipped than where you studied

Nice to Have (Not Required)

Exposure to healthcare claims, medical coding (CPT/ICD-10/HCPCS), or RCM workflows
Experience in a regulated data environment (HIPAA, SOC 2, or similar)
Fine-tuning, distillation, or building smaller task-specific models where a general LLM is too slow or expensive

What Success Looks Like in 6 Months

You own at least one production model/agent end-to-end — from data to deployment to monitoring
You've meaningfully improved an accuracy, latency, or automation-rate metric on a live customer workflow
You can independently debug a production issue in your system without pulling in a senior engineer to hold your hand

How to Apply

Please click on the link and fill in the details: https://forms.gle/sM2yRHGzxxWH4kVG7

About CombineHealth

Before You Apply — Read This First

4 days/week in our HSR Layout, Bangalore office. Remote or fully-hybrid-light setups won't work for this team.

If either of these is a dealbreaker, save yourself and us the time — this isn't the role.

What You'll Actually Do

Build AI agents that read unstructured clinical and claims documentation and produce structured, auditable outputs — codes, claim decisions, denial root causes, appeal drafts
Design the agent architecture: multi-step orchestration, tool/function calling, retrieval, and context engineering — plus the prompt and evaluation loop that makes it reliable, not just good in a demo
Build eval harnesses and regression suites with hard accuracy bars. "The eval moved" is the unit of progress here — if you can't measure whether a change helped, it didn't happen
Own reliability and observability in production: tracing, logging, latency, cost, failure handling — and yes, the pager when your agent breaks at 11pm US time
Design confidence-threshold and human-in-the-loop review systems — this is regulated, high-stakes data; "mostly right" isn't good enough, and every decision needs a traceable rationale
Work directly with product and RCM domain experts to translate messy real-world payer behavior into agent requirements
Own your systems end-to-end: if it breaks in production, you're the one who gets paged, not someone else

What We Need

5+ years shipping production software, with real depth building LLM-based systems — agents, RAG, structured extraction. Production systems with real users and real data, not POCs or research. Tenure in classical ML or CV is not what we're screening for.
Strong engineering fundamentals. You write production-grade Python, not notebooks. You design systems, handle failure paths, and care about latency and cost.
Fluency in the LLM application stack: prompt and context engineering, tool/function calling, retrieval, and — most important — eval-driven development. You know how to measure whether a change actually improved the system.
Comfortable owning your system in production end-to-end: deployment, observability, tracing, debugging live issues, cloud (AWS/GCP/Azure). We're not expecting a dedicated infra specialist, but you own your agent in production — you don't throw it over a wall.
Experience with unstructured/messy real-world text where the output has to be accurate and explainable. Healthcare is a bonus, not a requirement — legal, financial services, insurance, or other high-stakes-accuracy domains count.
Demonstrated ability to work independently and make judgment calls. We will not micromanage you, and we need people who don't need it.
Comfortable in a fast-moving startup environment: ambiguity, shifting priorities, and building things that don't have a textbook answer yet.
Degree from a Tier-1 engineering institute (IIT/IISc or equivalent) is a plus, but we care far more about what you've shipped than where you studied

Nice to Have (Not Required)

Exposure to healthcare claims, medical coding (CPT/ICD-10/HCPCS), or RCM workflows
Experience in a regulated data environment (HIPAA, SOC 2, or similar)
Fine-tuning, distillation, or building smaller task-specific models where a general LLM is too slow or expensive

What Success Looks Like in 6 Months

You own at least one production model/agent end-to-end — from data to deployment to monitoring
You've meaningfully improved an accuracy, latency, or automation-rate metric on a live customer workflow
You can independently debug a production issue in your system without pulling in a senior engineer to hold your hand

How to Apply

Please click on the link and fill in the details: https://forms.gle/sM2yRHGzxxWH4kVG7

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About CombineHealth

Founded :

2022

Type :

Product

Size :

20-100

Stage :

Raised funding

About

N/A

Company social profiles

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