Senior AI/ML Engineer

ApplyMint; Bengaluru; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

RapidClaims in Bengaluru, India, is expanding its production AI platform for US healthcare revenue cycle management. We are hiring a Senior AI/ML Engineer to own end-to-end LLM, retrieval, and evaluation layers, building scalable, auditable systems for coding, edits, denials, and appeals.

You will deploy self-hosted LLMs, design knowledge graphs and embedding-based retrieval, and implement robust evaluation and observability in a regulated environment.

Qualifications

• 5+ years in ML/AI engineering, including production LLM systems.
• Experience deploying self-hosted LLMs (vLLM, SGLang, TensorRT-LLM).
• Design embedding-based retrieval and knowledge graphs for grounded LLM applications.
• Proven ownership of evaluation infrastructure — offline benchmarks, online monitoring, drift and regression detection.
• Strong Python, PyTorch, and Hugging Face experience.
• Production experience with monitoring, incidents, and system ownership.

Responsibilities

• Deploy, fine-tune, and operate open-source models as our primary inference stack.
• Design and maintain the knowledge graph encoding clinical and payer rules, and embedding-based retrieval.
• Build continuous evaluation pipelines and monitor drift, hallucinations, and output quality in production.
• Design prompts and context pipelines for coding, claim edits, denial classification, and appeal drafting.
• Define deterministic vs. LLM-based tool boundaries and ensure auditability in MCP workflows.

Skills

5+ years ML/AI engineering

Production LLM systems

Self-hosted LLM deployment

Python

PyTorch

Hugging Face

Monitoring & incidents

Evaluation infrastructure

LLM observability

Embeddings / knowledge graphs

Tools

vLLM

SGLang

TensorRT-LLM

Neo4j

ArangoDB

Langfuse

LangSmith

Arize

Job description

RapidClaims is a leader in AI-driven revenue cycle management, transforming how US healthcare providers run mid- and end-revenue cycle operations — from medical coding and charge capture through claim scrubbing, denials management, appeals, and payment posting.

The company has raised $11 million in total funding from top investors, including Accel

and Together Fund.

Join us as we scale a cloud-native platform that runs self-hosted, fine-tuned Large Language Models, knowledge graphs, and embedding-based retrieval over millions of clinical notes, claims, and payer-policy documents every month. You’ll engineer autonomous pipelines that parse clinical records and translate into codes, provide documentation improvement parameters, and even solve for denials with autonomous calling if needed; Tackle the

deep-domain challenges that make clinical and RCM AI one of the most rewarding problems in tech.

Senior AI/ML Engineer- Job Overview

We are hiring a Senior AI/ML Engineer to own the end-to-end applied LLM, retrieval, and evaluation layer of our healthcare AI platform. You will build production systems that automate mid- and end-revenue cycle workflows for US healthcare spanning coding, claim edits, denials triage, appeal generation, and payer-rule reasoning.

This is a production engineering role (not research) focused on building scalable,

auditable, and cost-efficient LLM systems in a regulated healthcare environment

What You’ll Own

1. Self-Hosted LLM Infrastructure

• Deploy, fine-tune, and operate open-source models (Llama, Qwen, MedGemma, and successors) as our primary inference stack
• Work with vLLM / SGLang / TensorRT-LLM for serving at scale, with disciplined attention to throughput, tail latency, batching, KV-cache, and GPU economics
• Own fine-tuning workflows end-to-end (SFT, LoRA, QLoRA, DPO) on clinical notes, claims, and payer-rule data
• Optimize GPU usage, latency, batching, and cost; make build-vs-buy and hosted-vs-self-hosted trade-offs explicit and measured

2. Knowledge Graphs & Embedding-Based Retrieval

• Design and maintain the knowledge graph encoding ICD-10-CM, CPT, HCPCS, modifiers, HCC, NCCI edits, LCD/NCD policies, and payer-specific rules — and the relationships between them
• Build embedding-based retrieval over clinical notes, historical claims, denial reasons, and payer-policy corpora — including chunking, embedding model selection, hybrid search, and re ranking
• Combine graph traversal and dense retrieval so every coded line, scrubbed edit, and appeal response is grounded in auditable evidence
• Own ingestion, versioning, and quality of underlying knowledge sources (CMS, AHA, AMA, NCCI, payer bulletins)

3. Evaluation & Monitoring

• Build continuous evaluation pipelines that gate every model, prompt, retrieval, and graph change before production
• Run offline eval suites grounded in coder- and biller-validated labels; use LLM-as-judge where appropriate, calibrated against human ground truth
• Monitor drift, hallucinations, regressions, and output quality in production; operate shadow-mode rollouts and per-cohort accuracy tracking (specialty, payer, chart type)
• Track business metrics: chart-level and opportunity-level coding accuracy, denial rate impact, clean-claim rate, cost per chart, and end-to-end latency

4. LLM Systems & Prompt Engineering

• Design prompts and context pipelines for coding (CPT, ICD, HCC, E/M), claim edits, denial classification, and appeal drafting
• Implement structured outputs (JSON, function calling, constrained decoding) on top of the self-hosted stack
• Apply RAG over medical coding standards (CMS, ICD-10, AHA, NCCI) and payer policies, grounded in the knowledge graph and embedding stores
• Treat prompts as a thin, well-versioned, well-evaluated layer — never the load-bearing piece

5. Agentic Workflows & Tooling — MCP

• Build MCP servers for internal tools: code lookup, NCCI / rule checks, payer logic, eligibility, denial classification
• Design multi-step agent workflows with audit trails and human-in-the-loop checkpoints for coder, biller, and AR-analyst review
• Define deterministic vs. LLM-based tool boundaries for reliability — reliability comes from knowing which is which

What We’re Looking For

Must-Have

• 5+ years in ML/AI engineering, including 6+ months in production LLM systems
• Hands-on experience deploying and operating self-hosted LLMs (vLLM, SGLang, TensorRT-LLM, or equivalent)
• Strong experience designing embedding-based retrieval and/or knowledge graphs for grounded LLM applications
• Demonstrated ownership of evaluation infrastructure — offline benchmarks, online monitoring, drift and regression detection
• Strong Python + PyTorch + Hugging Face experience
• Production experience with monitoring, incidents, and system ownership

Strongly Preferred

• Fine-tuning experience (SFT, LoRA, QLoRA, DPO) on domain-specific corpora
• Experience with graph databases (Neo4j, ArangoDB, or equivalent) and graph-aware retrieval
• Experience with vector databases and hybrid search (BM25 + dense, rerankers)
• Familiarity with LLM observability tools (Langfuse, LangSmith, Arize, Braintrust, or in-house equivalents)
• Exposure to healthcare, RCM, claims, or other regulated domains
• Experience with MCP or similar tool-orchestration frameworks
• Strong prompt-engineering and LLM-evaluation instincts

What We Offer

• Work on high-impact healthcare AI systems used in real billing and RCM workflows
• Ownership of production LLM, retrieval, and evaluation systems end-to-end
• Solve real-world problems with real constraints (cost, latency, compliance, auditability)