About the role

VIVIO Health is transforming how it operates with AI, and we are hiring a senior engineer to build the features that make that real: language-model capabilities for our clinical, client success, and member experience teams, built on AWS Bedrock and related models and shipped to real users. Your job is to take a business problem from one of those teams, work out where a model helps and where it does not, and deliver something the team uses every day. This is not a research role. The AI work lives inside our production platform, so you will also ship application features, work with the integrations that exchange data with our partners and build the test and quality practices that keep all of it reliable.

Our clinical programs are the first area we are transforming with AI, so your early work will be with the clinical team and the documents, reviews, and decisions that run through it. Other business areas follow as the platform stabilizes.

The role is remote, with structured written communication as the default working mode. You report to the Head of Engineering and work directly with the business owners of the problems you solve.

What you will do

Build and extend LLM-backed capabilities on AWS Bedrock and related models: document OCR and extraction on clinical and claims paperwork, summarization and decision support for clinical review, Knowledge Bases for retrieval over plan and policy documents, and workflow automation for internal teams
Establish and maintain evals for the AI features you ship, so we know when quality drifts
Handle the practical side of production AI: structured output, failure modes, cost, latency, and knowing when a deterministic approach beats a model
Use agentic coding tools (Claude Code and Kiro) as a core part of your workflow, and own the verification of what they produce
Design, build, test, and ship features in our Python and FastAPI back end on MySQL and AWS, and our Jinja2, Tailwind, and Alpine.js front end, with a bias toward fixing what is broken before adding what is new
Work with the integrations that exchange eligibility, accumulator, and claim data with carriers and pharmacies, including NCPDP D.0 and X12 270/271 transactions and the file formats behind them
Put test and lint gates in place where they are missing, and treat CI as part of the definition of done
Treat PHI handling as a first-class design constraint in everything you build
Write clear specs, prompts, and design notes; at this level, written communication is an engineering skill

How we work

Development happens in a managed Linux environment that you reach by remote desktop from a Windows endpoint; PHI never lands on a laptop. You need to be fluent in both: Linux for the actual engineering, Windows as the workstation you get there from.

What we need

7+ years building production software, with real ownership of systems that other people depend on
Strong Python with production FastAPI (or a comparable async framework) experience, solid SQL on MySQL or Postgres, and working knowledge of AWS
Comfortable in a server-rendered front end (Jinja2, Tailwind, Alpine.js or htmx); we are not looking for a React specialist
Fluent in Linux for development and comfortable working from a Windows workstation over remote desktop into a secure environment
Daily use of AI coding tools on real work, and a clear point of view on where they help and where they fail
At least one LLM-powered feature shipped to real users, including how you evaluated it and what went wrong
Practical knowledge of prompt and context engineering, retrieval, embeddings, and structured output
Working knowledge of healthcare data exchange standards and file-based integrations: NCPDP D.0, X12 270/271, SFTP, fixed-width and delimited formats, and reconciliation
Direct communicator who names tradeoffs plainly and asks when something is unclear

Nice to have

Healthcare or other regulated-data experience (HIPAA, PHI, SOC 2)
Pharmacy benefits, claims, or clinical program experience
Self-hosting open-source models (Llama, Qwen, Mistral, or similar) with vLLM, Ollama, or TGI, including GPU sizing, quantization, and serving inside a private network
Fine-tuning or adapter training on domain data
Classical ML for prediction or scoring (scikit-learn, XGBoost), with model monitoring in production
Master's degree in computer science or a related field

How we hire

An HR phone screen, a technical interview that includes a short practical exercise (extract structured data from messy documents and show us your evals), a conversation with the Head of Engineering, and a final conversati

Senior Software Engineer, Applied AI at VIVIO Health