Senior AI/ML Engineer

The Senior AI/ML Engineer is responsible for building and operating the foundation that puts AI to work across the organization: the Azure infrastructure AI workloads run on, the patterns every engineering team uses to develop with agents, and UNOS’s own production agents., * Provision AI infrastructure on Azure as code, including model deployments in Microsoft Foundry and Databricks, GPU capacity, agent runtimes, as well as the networking, identity, and secrets around them.

• Stand up open-weight and frontier models on UNOS infrastructure under a pattern approved by Security and Privacy for protected health information (PHI).
• Develop the technical policies and build controls into the platform itself: per-agent identities, least-privilege access, private endpoints, allow-listed models, and audit evidence produced as a byproduct of normal use.
• Run shared model access across providers, with routing, quotas, tracing, and cost attributed to each team and use case.
• Build sandboxed environments where engineers run coding agents against real repositories, with no production credentials inside.
• Define UNOS’s standard patterns for agentic development and publish them as working templates and reference implementations.
• Integrate AI into CI/CD for pull-request review, test-failure triage, and security findings.
• Build and ship UNOS’s own agents, in UNOS One products and for internal operations, including tool access, retrieval, orchestration, and human approval where a decision requires it.
• Build the path from prototype to production in UNOS One, so a working proof of concept ships without being rebuilt.
• Own evaluation and release for agents and conventional models alike, including test sets, staged rollout, rollback, and monitoring.
• Baseline and track the time from idea to working proof of concept, and the quality of what ships.
• Teach agentic development across UNOS through pairing, working sessions, and code review, and mentoring engineers in the AI function.
• Recommend build-or-buy decisions on AI platforms and tools, with cost and risk stated.
• Perform other duties and support additional administrative priorities as assigned, consistent with the general scope and level of the position.

• Approximately six (6) years of experience building and running production systems, including at least three (3) years owning cloud infrastructure as code., * Production cloud depth, Azure preferred (equivalent AWS or GCP experience considered), with Terraform or Bicep: virtual networks, private endpoints, managed identities, role-based access, secrets, and policy.
• Strong handle on fundamental principles of AI/ML (e.g. train/test/validation, overfitting, calibration, eval methods)
• Hands-on provisioning of AI workloads, including model serving endpoints, quota and capacity, GPU compute, and cost controls.
• An LLM-based system built and run in production, past the demo stage, including tool calling or agents, retrieval, evaluation, and tracing.
• Experience working under HIPAA, HITRUST, SOC 2, FedRAMP, or a similar control framework, translating controls into configuration and evidence.
• Daily practice with coding agents such as Claude Code, GitHub Copilot, Codex, or Cursor.
• Strong proficiency in Python, containers, and Kubernetes or an equivalent orchestrator.
• Demonstrated record of changing how a team works without managing it, through establishing useful repeatable patterns that reduce team burden and shorten time to solution or insight.
• Ability to choose what to build first with incomplete information and ship working versions early.

Additional Skills & Qualifications

• Experience with Microsoft Foundry, Foundry Agent Service, Azure OpenAI, or Azure API Management as an AI gateway.
• Experience with Databricks, including Unity Catalog, Model Serving, and MLflow.
• Experience serving open-weight models with vLLM, SGLang, or similar, including quantization and GPU capacity planning.
• Experience building MCP servers or other tool interfaces over existing APIs and legacy systems.
• Experience with agent security, including prompt-injection defenses, tool permissions, and sandboxing.
• Experience operating conventional machine learning: training pipelines, model registries, and drift monitoring.
• Experience with HL7 FHIR or EHR integration; organ donation and transplant experience is a plus.

Education

• Bachelor’s Degree in Computer Science,Engineering or a related field preferred; equivalent experience considered.

Physical Requirements

• General office demands
• Prolonged periods of sitting at a desk and working on a computer.
• Frequent reaching, handling, and fine manipulation for using office equipment, filing, and managing paperwork.
• Manual dexterity sufficient to operate a keyboard, mouse, and other office tools.
• Occasional standing, walking, and bending.
• Ability to lift up to 10-20 pounds occasionally.
• Vision abilities required include close vision for computer work and reading documents.
• Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.