About the company and the product
India Health Link
is building a new multi-tenant Hospital Management Information System (HMIS) — 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. 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 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 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.
