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.