Senior Machine Learning Engineer - Agentic AI

UT MD Anderson Cancer Center•Houston, TX

About The Position

As a Senior Machine Learning Engineer – Agentic AI within Data Impact & Governance, you will be at the forefront of designing and operating the platform capabilities that enable autonomous and semi‑autonomous AI systems to function reliably across clinical, research, and operational domains. This role offers a rare opportunity to build enterprise‑wide agentic AI platforms in a regulated healthcare environment—where correctness, safety, governance, and auditability matter as much as innovation and scale. You will influence technical standards, platform architecture, and operational safeguards that shape how agentic AI is adopted across one of the world’s leading cancer centers.

Requirements

Experience building AI or ML platforms that serve multiple downstream teams and production workloads.
Strong proficiency in Python and integration of modern ML frameworks (e.g., PyTorch) with large language models and agent systems.
Hands‑on experience with agentic AI frameworks such as LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or equivalent.
Working knowledge of agentic AI protocols and interoperability standards (e.g., MCP, agent‑to‑agent communication, structured tool invocation).
Experience implementing planner‑executor loops, hierarchical agents, and multi‑agent coordination patterns.
Familiarity with workflow orchestration tools (Airflow, Prefect, Temporal) and distributed execution frameworks (Ray or equivalent).
Experience deploying containerized AI platforms using Kubernetes in enterprise cloud environments with lineage, auditability, and controlled promotion to production.
Ability to reason at the systems and platform level, balancing safety, performance, flexibility, and usability.
Experience designing quantitative evaluation strategies for agentic systems, including success rates, latency, cost, recovery behavior, and safety metrics.
Strong understanding of enterprise data governance, security, and privacy requirements, including healthcare and health IT considerations.
Ability to identify systemic risks stemming from agent autonomy, non‑determinism, tool access, and multi‑agent interactions.
Experience analyzing failure modes caused by prompt drift, model updates, tool changes, and cross‑system dependencies.
Collaborate effectively with architects, applied MLEs, data scientists, software engineers, and IT partners.
Produce clear documentation covering platform architecture, APIs, integration patterns, validation frameworks, and operational runbooks.
Communicate platform capabilities, risks, and limitations to leadership and partner teams.
Contribute to internal standards and shared practices that improve safety, scalability, and consistency of agentic AI development.
Provide hands‑on technical guidance, mentorship, and troubleshooting support to platform adopters.
Present technical and non‑technical concepts clearly in meetings and institutional forums.
Bachelor's degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline.
Five years of experience in machine learning engineering, data science, data engineering, and/or software engineering. With Master's degree, three years’ experience required. With PhD, one year of experience required.

Nice To Haves

Healthcare background
Master’s degree or PHD with a concentration in Science, engineering, or related field.
Experience designing, deploying, and maintaining agentic AI systems that operate autonomously and collaboratively across distributed environments.
Experience in monitoring and troubleshooting autonomous agents post-deployment, including performance degradation, clinical incidents, model updates, or corrective actions.
Experience raising the technical bar for team members, such as establishing reproducibility practices, review standards, or shared patterns.
Experience technically evaluating third-party agentic AI platforms within clinical workflows.

Responsibilities

Lead the design, evolution, and operation of the enterprise agentic AI platform in collaboration with enterprise architects and platform ML engineers.
Build platform components that enable interoperability between first‑party and third‑party agents, including identity, state, memory, tool access, orchestration, auditability, and policy enforcement.
Define and document standardized integration patterns connecting agents with enterprise business systems, data platforms, APIs, and health IT systems.
Provide reusable platform services, reference implementations, and SDKs that reduce risk and accelerate delivery for applied teams.
Design and operate validation and de‑risking frameworks, including simulation, sandboxing, shadow execution, canary releases, and continuous behavior monitoring.
Establish and enforce platform standards for agent development, including interfaces, execution contracts, evaluation hooks, safety constraints, and observability requirements.
Participate in platform governance, release coordination, and incident response, supporting investigation and remediation of agent‑related failures.
Implement platform safeguards such as fallback mechanisms, rollback strategies, approval gates, rate limiting, audit trails, and kill‑switch capabilities.
Partner with software engineering, security, IT, and health IT stakeholders to deploy agentic AI capabilities in secure enterprise environments.
Support responsible AI practices through traceability of prompts, policies, tools, models, agent actions, and documentation of known failure modes and limitations.