About the job :

Autonomize AI is building the intelligence layer for healthcare.

Our Genesis platform replaces brittle, manual knowledge workflows with AI agents that reason, retrieve, and act - reducing administrative burden so clinicians can focus on patients.

We're looking for engineers who don't just integrate AI into software - they think in agents, design for inference, and treat LLMs as first-class infrastructure.

What This Role Is :

- You'll architect and ship across the full Genesis stack: agentic pipelines, backend APIs, data infrastructure, and clinical-facing UI.

- You'll work directly with founders and customers.

- You'll own things end-to-end.

This is not a role where you bolt AI onto existing CRUD.

You'll be making foundational decisions about how intelligent systems are designed, evaluated, and operated at scale in a regulated industry.

You'll Thrive Here If :

- AI-native engineering is your default mode

- You've built production systems where LLMs are doing real work - not demos, not PoCs

- You've designed and shipped RAG pipelines, multi-agent workflows, or tool-using agents in production

- You understand prompt engineering as an engineering discipline: versioning, evaluation, regression testing

- You've instrumented AI systems for observability - latency, token usage, hallucination rate, drift

- You can reason about model tradeoffs (context length, cost, latency, accuracy) and make architectural calls accordingly

- You've worked with LLM SDKs (OpenAI, Anthropic, Bedrock, etc.) and agentic orchestration frameworks (LangChain, LlamaIndex, CrewAI, or similar)

You build robust backend systems :

- 4+ years building production web applications from scratch

- Deep Python proficiency; comfortable with FastAPI, Django, or Flask in production

- Experience designing APIs that serve both humans and AI agents (tool schemas, structured outputs, streaming)

- Async-first thinking: asyncio, task queues, event-driven architectures

- Kafka, Redis, or ActiveMQ for real-time data movement

- Postgres, Elasticsearch, MongoDB, or graph databases (Neo4j, TigerGraph) in production

You operate at cloud scale :

- Docker and Kubernetes in production - this is a hard requirement

- At least one public cloud (AWS, Azure, GCP) with real operational experience

- Microservices and cloud-native design patterns

- You've been on-call.

- You know what a bad deploy feels like at 2am.

You can ship a frontend when the product demands it :

- React, TypeScript, or modern JS frameworks

- Enough frontend fluency to build clinical interfaces without a dedicated frontend handoff

Bonus :

You've Done This Before :

- Led a small engineering team - mentored, reviewed, unblocked

- CKAD or equivalent Kubernetes certification

- ML/DL model deployment experience (PyTorch, scikit-learn)

- Built evaluation harnesses or used MLflow, LangSmith, or similar for AI observability

- Healthcare domain experience (FHIR, HL7, clinical workflows)

What We Value Above Credentials :

- Bias toward action - you ship, then iterate.

- You learn by doing, not by planning.

- Owner mentality - you don't wait for permission.

- You identify what needs to exist and build it.

- Intellectual honesty - you'd rather say "I don't know, let me find out".

- When unsure, seek the right information from your peers or leaders.

- Async-first communication - you write clearly, document decisions, and work well across time zones.

What You'll Get :

- Ground floor equity in a VC-backed healthcare AI company growing fast

- Full-stack ownership - no handoffs, no silos, no "that's not my team"

- Direct access to founders and customers - your technical decisions will be seen and felt

- Professional development budget - conferences, courses, certifications, books

- Flexible-friendly culture built around output, not hours

If you've been waiting for a role where AI isn't a feature - it's the foundation - this is it