Senior AI/ML Engineer

UNITED NETWORK FOR ORGAN SHARING•n/a, OR

1d•$135,000 - $155,000•Remote

About The Position

The Senior AI/ML Engineer builds and operates the foundation for deploying AI across UNOS. This includes Azure infrastructure for AI workloads, reusable patterns for agentic development, and production agents built for UNOS. The role supports conventional machine learning, generative AI workflows, and autonomous agentic solutions, ensuring they operate safely with protected health information (PHI), and with evaluation, monitoring, and cost controls. It requires strong cloud and infrastructure engineering expertise applied to AI technologies and offers an opportunity to shape AI and agentic development across healthcare, logistics, clinical science, and biotech.

Requirements

Approximately six years of experience building and running production systems, including at least three years owning cloud infrastructure as code.
Production cloud expertise, preferably Azure; equivalent AWS or GCP experience is considered. Experience with Terraform or Bicep and virtual networks, private endpoints, managed identities, role-based access, secrets, and policy is required.
Strong understanding of fundamental AI/ML principles, such as train/test/validation, overfitting, calibration, and evaluation methods.
Hands-on experience provisioning AI workloads, including model-serving endpoints, quota and capacity, GPU compute, and cost controls.
Experience building and operating an LLM-based system in production beyond 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 and 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 ability to change how a team works without managing it by establishing repeatable patterns that reduce team burden and shorten time to solution or insight.
Ability to prioritize what to build with incomplete information and ship working versions early.
Bachelor’s degree in Computer Science, Engineering, or a related field preferred; equivalent experience is considered.

Nice To Haves

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, including training pipelines, model registries, and drift monitoring.
Experience with HL7 FHIR or EHR integration.
Organ donation and transplant experience.

Responsibilities

Provision AI infrastructure on Azure as code, including model deployments in Microsoft Foundry and Databricks, GPU capacity, agent runtimes, networking, identity, and secrets.
Deploy open-weight and frontier models on UNOS infrastructure using patterns approved by Security and Privacy for protected health information (PHI).
Develop technical policies and platform controls, including per-agent identities, least-privilege access, private endpoints, allow-listed models, and audit evidence generated through normal use.
Operate shared model access across providers with routing, quotas, tracing, and costs attributed to teams and use cases.
Build sandboxed environments for engineers to run coding agents against real repositories without production credentials.
Define and publish standard patterns, working templates, and reference implementations for agentic development.
Integrate AI into CI/CD for pull-request review, test-failure triage, and security findings.
Build and ship UNOS agents for UNOS One products and internal operations, including tool access, retrieval, orchestration, and human approval where needed.
Build a path from prototype to production in UNOS One so proofs of concept can ship without being rebuilt.
Own evaluation and release processes for agents and conventional models, including test sets, staged rollout, rollback, and monitoring.
Baseline and track time from idea to working proof of concept and the quality of shipped work.
Teach agentic development through pairing, working sessions, and code review, and mentor engineers in the AI function.
Recommend build-or-buy decisions for AI platforms and tools, stating costs and risks.
Perform other duties and support additional administrative priorities as assigned, consistent with the general scope and level of the position.