Machine Learning Engineer - Platforms

Role Overview

As a Machine Learning Engineer specializing in Platforms within the Data Impact & Governance group, you will lead the design and expansion of the institution-wide AI/ML enterprise platform that supports clinical, research, and operational machine learning applications. This is a technically hands-on position influencing data science workflows across the entire organization to foster secure, efficient, and impactful AI implementation.

Responsibilities

• Develop, administer, and maintain the AI/ML infrastructure including Dataiku, Kubernetes, and Azure ensuring robustness, scalability, and seamless integration with other institutional systems.
• Orchestrate the deployment and operation of training, inference, and pipelines in Dataiku across Azure cloud and Kubernetes clusters hosted on-premises.
• Establish and sustain reproducible MLOps workflows emphasizing version control, governance, and governance across the model lifecycle.
• Manage containerized environments utilizing Docker and Kubernetes to support data science workloads effectively.
• Provide technical platform support, troubleshoot environment or dependency-related problems for data scientists and ML engineers.
• Monitor and optimize platform performance, cost-efficiency, security, and ensure compliance with enterprise and regulatory policies.
• Construct scalable feature engineering, model validation, tracking, and testing pipelines within Dataiku, Kubernetes, and Azure.
• Apply problem-solving skills to debug and resolve complex platform and pipeline challenges.
• Assist with healthcare data integration leveraging standards such as HL7, FHIR, or DICOM when necessary for model development.
• Document and share best practices and platform knowledge through training sessions and cross-team collaboration.
• Support analytical processes by facilitating data access, reviewing project inputs, and aiding interpretation.
• Communicate clearly about platform status, risks, and mitigation during meetings and collaborative forums.
• Collaborate efficiently with leadership, peers, and users across technical and non-technical groups.
• Perform any additional duties to advance the AI/ML platform, MLOps, and institutional data science efforts.

Qualifications

• Education: Bachelor's degree in Computer Science, Software Engineering, Data Science, Physics, Mathematics & Statistics, or closely related fields is required. A Master's degree in similar disciplines is preferred.
• Experience: Minimum of three years in machine learning engineering, data science, data engineering, or software engineering is required; possession of a Master's degree reduces the required experience to one year. PhD holders are exempt from experience requirements.
• Preferred Skills: Experience in healthcare, familiarity with MLOps platforms and cloud AI certifications, proficiency with CI/CD pipelines and AI lifecycle automation. Hands-on skills with Azure services including Azure Kubernetes Service and Azure ML or equivalent, and Kubernetes expertise are advantageous.

Benefits & Additional Information

• Comprehensive benefits encompass medical and dental coverage, paid leave, retirement plans, tuition assistance, and recognition programs.
• This role carries responsibility for safeguarding critical infrastructure as outlined in Texas law, necessitating routine security assessments and compliance.
• The organization is committed to equal employment opportunities across all protected classes and complies with applicable institutional and legal anti-discrimination policies.
• Requisition ID: 178799
• Employment Status: Full-Time, Regular
• Work Schedule: Day shifts
• Salary Range: $123,000 minimum to $185,000 maximum with a midpoint of $154,000 annually
• FLSA Status: Exempt (not eligible for overtime)
• Fund Type: Hard funding
• Work Location: Remote within Texas
• This is a pivotal position eligible for referral bonuses and relocation assistance.