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
- 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. 
