Full Stack Engineer

Full Stack Engineer (with Infrastructure)

About Chattrly

Chattrly is a healthtech startup building the health intelligence ecosystem for patients and

clinicians. Two interconnected products, one thesis: Patient data in India is scattered across

thousands of labs, hospitals, and apps that don't talk to each other. We're closing that gap on both

sides — giving patients a unified view of their own health, and giving clinicians the intelligence layer

that turns fragmented data into instant, contextual insight. Every response we generate has to be

grounded, verifiable, and clinically safe — there is no room for hallucination when a doctor is making

a decision.

What You'll Actually Work On

You'll move across the full stack and occasionally into infrastructure. Concretely:

Backend

Build and extend API endpoints that power the clinical Q&A experience and the patient app

Work inside our AI orchestration pipeline — the layers that turn a clinician's free-text

question into a grounded, structured, trustworthy answer

Design and evolve database schemas, write migrations against live tables, and ship them

safely to production

Use caching and async task queues for background work like document processing, OCR, and

summarisation

AI / LLM Pipeline

Integrate, tune, and evaluate calls to large language models — routing, structured output,

narrative generation, and verification

Work on our hallucination defence layer: deterministic grounding checks plus model-based

gates that catch hallucinations before they reach the clinician

Help with prompt engineering — we treat prompts as code: versioned, evaluated, and

deployed deliberately, not edited live

Build evaluation harnesses so we can measure regression every time we change a prompt or

swap a model

Mobile & Web

Ship user-facing features across our patient mobile app (iOS + Android) and our clinical web

platform — onboarding, document uploads, the chat surface, and more

Work with native device capabilities like the camera, document scanning, and secure on-

device storage

Debug platform-specific issues across iOS, Android, and the web — cold-boot lifecycle,

modal behaviour, keystore quirks, and the long tail of cross-platform weirdness

Infrastructure (occasionally)

Help maintain our cloud deployments and the services that back them

Write and review container builds, debug runtime issues, tune concurrency and timeouts

under real load

Touch our deploy scripts (idempotent, atomic-swap, auto-rollback on smoke-test failure)

across staging and production

Work with secrets management, server-level configuration, and database operations against

live environments

Observability & Quality

Instrument features with structured logging — every model call, every critical path, with

cost, latency, and error telemetry feeding our dashboards

Write unit, integration, and end-to-end tests; our pipeline has an e2e harness that exercises

real clinical scenarios

Participate in code reviews. Every PR goes through a structured review covering correctness,

clinical safety, security, scalability, and data completeness before merge

You're a Good Fit If You

B.Tech in CS and have 2-3 yrs of experience

Are comfortable writing both a backend language and a frontend language — not just one.

You'll genuinely move between them in the same week

Have deployed something — even a side project — to a cloud provider. Anything real, end-

to-end

Can read a container build file and an orchestration config without panicking

Understand REST APIs, relational databases, caching, and the basics of async / background

jobs

Are curious about how LLMs actually work in production — context windows, structured

output, grounding, the latency-vs-quality tradeoffs. You don't need to be an ML engineer,

but you should want to understand the pipeline

Can drop into a large unfamiliar codebase, read tests, search aggressively, and find your way

around without being hand-held

Care about correctness. In our domain, it kinda work is a bug

Remote-first, async-first. We respect your hours and how you work. Output over seat time