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MLOps Engineer Lead​/UKHC

Job in Lexington, Fayette County, Kentucky, 40598, USA
Listing for: UK HealthCare
Full Time, Seasonal/Temporary position
Listed on 2026-02-18
Job specializations:
  • IT/Tech
    AI Engineer, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 71000 USD Yearly USD 71000.00 YEAR
Job Description & How to Apply Below

Posting Details

  • Job Title: MLOps Engineer Lead/UKHC
  • Requisition Number: RE53257
  • Working Title: Machine Learning Operations Engineer Lead
  • Department Name: H3997:

    EVPHA Information Technology
  • Work Location: Lexington, KY
  • Grade Level: 13
  • Salary Range: $71,/year
  • Type of Position: Staff
  • Position Time Status: Full-Time
  • Required

    Education:

    BS
  • Required Related

    Experience:

    7 yrs
  • Required License/Registration/Certification: None
  • Physical Requirements: The physical requirements of this position include:
    Mobility to work from several locations depending on business needs; occasionally lifting, pushing, and/or pulling objects up to 50lbs; occasionally standing or walking with objects up to 10lbs; regularly sitting at a computer workstation for extended periods of time with regular repetitive motions (such as typing); occasionally dealing with combative/violent people; and occasional job‑related travel.
  • Shift: Primarily Monday through Friday 8am-5pm, with evening, night, and weekend requirements per departmental needs.
Job Summary

Responsible for leading enterprise‑wide machine learning operations strategy and execution. Provides expert‑level oversight of deployment platforms, monitoring systems, and operational standards for AI initiatives. Ensures scalability, governance, and resilience of ML pipelines across clinical, research, and business applications. Develops organizational best practices, influences technology roadmaps, and drives adoption of cutting‑edge MLOps capabilities. Coordinates strategic initiatives across technical and business domains to deliver high‑impact, reliable, and secure AI solutions.

Essential

Functions
  • Defines enterprise MLOps strategy including lifecycle management, CI/CD frameworks, and operational standards.
  • Leads implementation of monitoring and alerting systems for production ML models across multiple domains.
  • Establishes governance policies for model versioning, traceability, and compliance with regulatory frameworks.
  • Provides technical leadership and guidance to senior engineers and cross‑functional teams.
  • Oversees evaluation and integration of new MLOps tools, platforms, and cloud‑native solutions.
  • Partners with executives and business leaders to align ML operations with organizational strategy.
  • Directs development of reusable infrastructure, workflows, and automation frameworks.
  • Coordinates enterprise incident response and continuity planning for critical ML systems.
  • Mentors and advises engineering staff, fostering knowledge sharing and innovation in MLOps practices.
  • Performs other duties as assigned.

Note: Effective 7/1/2026, this position will be titled Machine Learning Operations Engineer Lead and will report through Information Technology Services in Beyond Blue.

Skills / Knowledge / Abilities
  • Deep expertise in MLOps frameworks, tooling, and deployment platforms.
  • Strong understanding of model lifecycle management, CI/CD for ML, monitoring, observability, and reliability engineering.
  • Ability to design scalable, resilient, and secure ML pipelines in regulated or high‑availability environments.
  • Knowledge of AI governance, risk management, and operational controls.
  • Strategic thinking with the ability to translate business and clinical needs into technical execution.
  • Strong leadership, influence, and cross‑functional collaboration skills.
  • Excellent communication skills, with the ability to align diverse stakeholders around shared AI/ML objectives.
Preferred Education/Experience
  • Significant experience leading machine learning operations (MLOps) at an enterprise scale, including strategy, architecture, and execution.
  • Demonstrated experience deploying, monitoring, and maintaining production‑grade ML models across complex environments (e.g., cloud, hybrid, or on‑prem).
  • Proven background supporting AI/ML solutions across multiple domains such as clinical, research, and business applications.
  • Experience establishing governance, operational standards, and best practices for ML pipelines, including security, compliance, and model lifecycle management.
  • Track record of influencing technology roadmaps and driving adoption of advanced AI/ML capabilities within large organizations.
  • Experience partnering with senior…
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