Senior Software Engineer, Applied AI

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 conversation with one or more of the business leaders you would support. Our interviews are a mix of phone calls, Video interviews and an on-site visit to our corporate office.

VIVIO does not provide employer support or sponsorship for any immigration related employment benefit for this role. 

Applicants must be currently authorized to work in the United States on a fulltime basis without the need for employer support, visa transfer support, DSO documentation or sponsorship now or in the future. This includes having the legal right to work in the United States without the need for VIVIO support or sponsorship for any immigration related employment authorization (e.g., H1B, O1, E3, H1B1, TN, F1 OPT, F1 STEM OPT, F1 CPT, E-2, E-3 etc.) now or in the future. 

Compensation and Benefits:

• The anticipated salary range for this position is $165K-$205K base. The actual salary offered will be determined by a variety of factors, including the candidate’s qualifications, experience, and the position's geographic location. 
• Bonus Eligible 
• Comprehensive Benefits Package: 
• Medical, Pharmacy, Dental, Vision 
• Life Insurance 
• Stock Options 
• 401(k) with Company Match 
• PTO 
• Full-time position 
• Dynamic, collaborative work environment 
• Opportunity to make a real impact at a growing Public Benefit Corporation 

VIVIO Health is an Equal Opportunity Employer. All information will be kept confidential according to EEO guidelines.