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AI that actually understands healthcare is transforming operations, one workflow at a time.
The Role
Architect and build Autonomize AI’s Genesis platform across agentic pipelines, backend APIs, data infrastructure, and clinical interfaces. Develop production RAG and multi-agent systems, robust asynchronous services, cloud-native microservices, AI observability, and frontend capabilities. Make architectural decisions involving LLM performance, cost, latency, evaluation, and reliability while operating systems at scale in a regulated healthcare environment.
Summary Generated by Built In
About Autonomize AI
The Opportunity
You're a Fit If You Have
Bonus Points
What We Value Above Credentials
What You'll Get
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.
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.
1) 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)
2) You build robust backend systems
- 6 + 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
3) 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
- 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)
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.
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, remote-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.
Skills Required
- 6+ years building production web applications from scratch
- Deep Python proficiency
- Production experience with FastAPI, Django, or Flask
- Production experience building LLM systems, RAG pipelines, multi-agent workflows, or tool-using agents
- Experience with prompt versioning, evaluation, regression testing, and AI observability
- Experience designing APIs for humans and AI agents, including tool schemas, structured outputs, and streaming
- Experience with asynchronous architectures, asyncio, task queues, or event-driven systems
- Experience with Kafka, Redis, or ActiveMQ
- Production experience with Postgres, Elasticsearch, MongoDB, or graph databases
- Production experience with Docker and Kubernetes
- Operational experience with at least one public cloud: AWS, Azure, or GCP
- Experience with microservices and cloud-native design patterns
- On-call operational experience
- Frontend development experience with React, TypeScript, or modern JavaScript frameworks
- Experience with OpenAI, Anthropic, Bedrock, or comparable LLM SDKs
- Experience with LangChain, LlamaIndex, CrewAI, or comparable agentic orchestration frameworks
- Small-team leadership, mentoring, code review, and unblocking experience
- CKAD or equivalent Kubernetes certification
- ML or deep learning model deployment experience with PyTorch or scikit-learn
- Experience building evaluation harnesses or using MLflow, LangSmith, or similar tools
- Healthcare domain experience with FHIR, HL7, or clinical workflows
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The Company
What We Do
Autonomize AI Agents & Copilots organize, contextualize and summarize unstructured data to reduce the administrative burden for healthcare knowledge workers to make data-driven decisions and improve patient outcomes. Our customers include health plans, providers and life sciences companies. Unlike generic AI systems retrofitted for healthcare, Autonomize deeply understands medical contexts, terminologies, and operational nuances. Our healthcare-focused AI Agents & Copilots augment knowledge work, drastically reducing administrative burden. Care management teams spend 78% less time per case, achieving an impressive 85% boost in case review efficiency. Prior authorization processes that traditionally take 20-30 minutes shrink to mere seconds, accompanied by an 80% reduction in manual errors, saving millions of dollars annually. Our AI Agents turn chaotic, unstructured healthcare data—clinical notes, PDFs, faxes, and claims—into structured, contextual information that informs decisions and actions. This has driven substantial real-world impact: organizations using Autonomize experience a 92% reduction in manual effort for care gaps and HEDIS chart reviews, dramatically improving compliance and STAR ratings. Autonomize AI is purpose-built for healthcare, transforming healthcare operations one workflow at a time through AI-native solutions that deliver immediate, scalable impact.
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