Senior AI/ML Engineer

United Network For Organ Sharing; Virginia; Presencial; USD 135.000 - 155.000; Jornada completa

Descripción de la vacante

United Network For Organ Sharing (UNOS) seeks a Senior AI/ML Engineer to build and operate AI infrastructure on Azure, enabling production-grade AI workloads and agent-based workflows.

The role covers model deployment, security, cost controls, and integration of AI into CI/CD, supporting PHI-protected environments and cross-functional teams in healthcare and biotech domains.

Formación

• Approximately six years of experience building and running production systems.
• At least three years owning cloud infrastructure as code.

Responsabilidades

• Provision AI infrastructure on Azure as code and deployments in Foundry and Databricks.
• Stand up open-weight and frontier models on UNOS infrastructure under security controls.
• Develop policies and controls for agent identities, access, endpoints, and auditability.
• Run shared model access with routing, quotas, tracing and cost attribution.
• Integrate AI into CI/CD for PR review, testing, and security findings.

Educación

Bachelor's Degree in Computer Science,Engineering or a related field

Herramientas

Terraform or Bicep

Azure

Kubernetes

Databricks

Foundry Agent Service

Azure OpenAI

MLflow

vLLM

Descripción del empleo

Job Details: Job Location: n/a, VA 00000, Position Type: Full Time, Education Level: 4 Year Degree or Equivalent Experience, Salary Range: $135,000.00 - $155,000.00 Salary, Travel Percentage: Negligible, Position Description

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.

UNOS will be building everything from conventional machine learning models to generative AI workflows to more autonomous agentic solutions; this role makes them run in production, safe for protected health information by default, with evaluation, monitoring, and cost under control.

This role requires a strong background in cloud /infrastructure engineering and the ability to apply that expertise to AI technologies while continuing to advance AI capabilities across UNOS. This role provides an opportunity to shape the trajectory of AI and agentic development and make a real difference in people’s lives, in a domain spanning healthcare, logistics, clinical science, and biotech.

Key Responsibilities

• 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.

Minimum Requirements

• Approximately six (6) years of experience building and running production systems, including at least three (3) years owning cloud infrastructure as code.

Critical Skills

• 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.