AI Engineer

India Health Link; Chennai District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

India Health Link in Chennai is building a multi-tenant Hospital Management Information System (HMIS) where AI features operate as plug-ins behind a protocol, ensuring the core system remains functional with AI features off. The role centers on delivering the plug-in runtime, LLM gateway, and three launch plug-ins.

You will contribute to the evaluation harness, dashboards, and cost controls while collaborating with clinical advisors to sign off outputs before hospital deployment.

Qualifications

• Experience shipping an LLM feature to real users and explaining quality metrics.
• Production service experience with structured outputs and schema constraints.
• Knowledge of prompt design and evaluation for medical/clinical contexts.

Responsibilities

• Treat every output as a suggestion and require human sign-off for deployment.
• Own quality measurement and ensure release readiness beyond testing.
• Collaborate with clinical advisors to sign off plug-ins before hospital rollout.
• Maintain data handling defensibility: what is sent, where processed, retention, and audit records.
• Control cost per plug-in per hospital and report monthly.

Skills

Python

LLMs

Prompt engineering

RAG

Evaluation

Tools

Amazon Bedrock

OpenAI API

Anthropic API

PostgreSQL

TypeScript

NestJS

AWS ECS Fargate

Job description

India Health Link is building a new multi-tenant Hospital Management Information System (HMIS) — one platform serving many hospitals. Artificial Intelligence (AI) is planned for every module, but the core system must work perfectly with every AI feature switched off. That means AI ships as plug-ins behind a protocol, with measured quality — not as code sprinkled through the clinical modules. One engineer owns that entire layer.

Our stack

Python for the AI services, Amazon Bedrock (Mumbai region) for large language models (LLMs), TypeScript and NestJS for the core, PostgreSQL 16, Amazon Web Services (AWS) — ECS Fargate, S3, EventBridge, OpenSearch.

What you will build

• The plug‑in runtime and registry: manifest and scopes, event subscriptions, and a per‑hospital install and kill switch.
• The LLM gateway: model routing, prompt and version management, cost and token controls, redaction of identifiers before anything leaves the boundary, and full auditing of every request.
• The three launch plug‑ins:
  • 1 Discharge Summary Assistant — drafts a summary from the encounter record for the doctor to edit and sign.
  • 2 Clinical Scribe — turns a consultation conversation into structured notes.
  • 3 Coding Assist — suggests International Classification of Diseases (ICD) and procedure codes from the note.
• The evaluation harness: a golden dataset, regression runs on every prompt change, and dashboards for acceptance rate and edit distance per hospital.

Responsibilities

• Treat every output as a suggestion. Nothing your layer produces is ever written directly into a clinical or financial record without a human accepting it.
• Own quality measurement. "It looked good in testing" is not a release criterion here — acceptance rate is.
• Work with the clinical advisors to sign off outputs before a plug‑in is enabled for a hospital.
• Keep patient data handling defensible: what is sent, where it is processed, what is retained, and the audit record for every call.
• Control cost per plug‑in per hospital, and report it monthly.

Requirements

• Python, and comfort building and operating a production service — not only notebooks.
• Practical LLM application patterns: prompt design, structured and schema-constrained output, retrieval-augmented generation (RAG), function calling, streaming, and evaluation.
• Has shipped at least one LLM feature to real users, and can describe how quality was measured and what was changed as a result.
• Amazon Bedrock, the Anthropic API or the OpenAI API in production.
• Pragmatism: you choose the smaller model and the simpler prompt when it does the job.

Good to have

• Speech-to-text for Indian-accented English and code-switched speech — the Clinical Scribe depends on it.
• Medical coding, clinical text, or de-identification work.
• TypeScript, so you can meet the core team halfway on the gateway.
• Evaluation tooling and experiment tracking.

Required Skills

Python Large Language Models (LLMs) LLM Evaluation & Prompt Engineering Retrieval-Augmented Generation (RAG) Amazon Bedrock / OpenAI / Anthropic API