Senior Machine Learning Engineer - Agentic AI

We need to output HTML fragment with sections: About the role? The posting includes description paragraphs. We need to use

for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections if posting has real specific content. We need to parse content: The posting includes: - Intro paragraph: "As a Senior Machine Learning Engineer - Agentic AI within Data Impact & Governance, you will be at the forefront..." This is about the role. Could be "About the role". - Then "What's in it for you?" with bullet points: Outstanding Benefits..., Enterprise-Level Impact..., Technical Leadership..., Career Growth & Visibility..., Responsible AI Innovation..., Collaborative Culture... Those could be Benefits? Actually they are more like selling points. Could be "Benefits" section? But there is also later "The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition." That's explicit benefits. - Then "The ideal candidate will have a healthcare background with at least 5 years of industry experience in data science and 3+ years as a Senior ML Engineer focused agentic AI systems" That's requirements/qualifications. - Then "Summary" paragraph: "The Senior Machine Learning Engineer - Agentic AI designs, evolves, and operates enterprise-scale agentic AI platform capabilities..." That's about role again. - Then "Major Work Activities Core Responsibilities" list of responsibilities. - Then "Competencies" with subcategories: Technical Expertise, Analytical Expertise, Oral & Written Communication. Those could be Skills. - Then "Education Required:" etc. That's Qualifications/Education. - Then "Preferred Experience:" etc. - Then benefits again. - Then other metadata: Requisition ID etc. Should be folded into short p or dropped. - Pay: Minimum Salary, Midpoint Salary, Maximum Salary given. So we have Pay section. - Schedule: "Work Week: Days" maybe indicates full-time days? Not specific schedule. Might be considered schedule but it's vague. The instruction: never create Pay/Schedule/Benefits sections unless posting actually states them. It states Pay (salary range). It states Benefits (medical, dental, PTO, retirement, tuition benefits). It states Schedule? It says "Work Week: Days". That's not specific hours; maybe we can include Schedule section with that info? It's minimal but it's stated. Could include. We must not include filler like "commensurate with experience". Not present. We need to preserve all substantive info, not invent or summarize away detail. So we need to include details from each section. We must not repeat job title as heading. We must not include cookie/navigation/legal/EEO etc. So remove EEO paragraph and link. We must not include application instructions. We need to output only HTML fragment, no /, no markdown fences. We'll structure:

About the role

... combine intro and summary? Might be duplicate. We should preserve all substantive info; we can combine but must not lose detail. Could have two paragraphs: one from intro, one from summary. Better:

About the role

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.

The Senior Machine Learning Engineer - Agentic AI designs, evolves, and operates enterprise-scale agentic AI platform capabilities that enable safe, scalable, and governed deployment of autonomous and semi-autonomous AI systems. The role focuses on platform architecture, interoperability, validation frameworks, and operational safeguards that allow internal and third-party agent systems to function reliably in production healthcare environments. This position operates at the intersection of autonomous AI behavior, enterprise systems integration, and regulated healthcare operations—where subtle failures can have systemic and high-impact consequences.

Then Responsibilities: list from "Major Work Activities Core Responsibilities". We'll create

Responsibilities

• ... each bullet. We need to capture each bullet: - 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. That's 10 bullets. Then Requirements/Qualifications: We have "The ideal candidate will have a healthcare background with at least 5 years of industry experience in data science and 3+ years as a Senior ML Engineer focused agentic AI systems". Also Education Required etc. Also Preferred Experience. We could have a section "Requirements" combining required experience and education. Or separate "Qualifications". The instruction says use headings like Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide. We have both required and preferred. Could put required under Requirements, preferred under Qualifications? Or combine. Better: Requirements: required experience and education. Qualifications: preferred experience and education? But we have both required and preferred education. Let's parse: Education Required: Bachelor's degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline. Preferred Education: Master's degree or PHD with a concentration in Science, engineering, or related field. Experience Required: 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. Preferred Experience: 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. Thus we can have:

  Requirements

  Healthcare background with at least 5 years of industry experience in data science and 3+ years as a Senior ML Engineer focused on agentic AI systems.

  Education: Bachelor's degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline.

  Experience: Five years of experience in machine learning engineering, data science, data engineering, and/or software engineering. With a Master's degree, three years' experience required; with a PhD, one year of experience required.

  Then

  Qualifications

  Preferred Education: 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.
  Then Skills: from Competencies sections: Technical Expertise, Analytical Expertise, Oral & Written Communication. We can list each as bullet under Skills. We need to preserve details: Technical Expertise includes: - 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. Analytical Expertise: - 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. Oral & Written Communication: - 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. We'll put these under

  Skills

  maybe grouped? Could just list all bullets. Benefits: from "What's in it for you?" bullet points and later benefits paragraph. We need to include all substantive benefits: From "What's in it for you?": - Outstanding Benefits: MD Anderson offers paid medical benefits, generous paid time off (PTO), and strong retirement plans, providing stability and long-term financial security. - Enterprise-Level Impact: Architect platform capabilities that support AI agents operating across complex health IT systems and enterprise workflows. - Technical Leadership: Shape standards, integration patterns, and guardrails governing agentic AI at organizational scale. - Career Growth & Visibility: Partner closely with enterprise architects, applied MLEs, data scientists, IT, and governance leaders on high-impact AI initiatives. - Responsible AI Innovation: Work in a mission-driven institution where responsible AI, safety, and trust are central to technology strategy. - Collaborative Culture: Join a highly skilled team that values intellectual rigor, mentorship, and cross-disciplinary collaboration. Then later: "The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition." We should combine benefits into a list. We must not include filler like "Outstanding Benefits:" as heading inside list