Director, Model Engineering – Operations

This is a fully remote position, open to applicants in United States.

📋 Description

• Spearhead the strategy, development, implementation, and enhancement of enterprise AI and machine learning solutions that bolster health plan operations and business goals.

• Direct the design, development, and production of ML and AI models across various use cases including risk adjustment, quality measures, care management, utilization management, fraud/waste/abuse, and member/provider experience.

• Manage the complete MLOps lifecycle on Databricks, encompassing feature engineering, feature stores, model training and versioning, CI/CD, deployment, and automated retraining.

• Implement model monitoring practices for drift, performance decline, bias/fairness, and champion-challenger frameworks.

• Facilitate the responsible integration of generative AI and LLM capabilities for analytics, member/provider tools, and operational automation.

• Establish engineering standards, design patterns, and reusable ML components.

• Collaborate with governance and security teams to maintain compliance with HIPAA, CMS, and NCQA regulations.

• Oversee model risk documentation and validation for regulatory reviews, audits, and quality submissions.

• Assume responsibility for the cost, performance, and reliability of the Databricks ML platform.

• Work alongside BI, data engineering, and data science teams on canonical data models and lakehouse architecture.

• Convert clinical, quality, finance, and operational challenges into defined ML engineering initiatives with success metrics and timelines.

• Articulate technical strategy, risks, and progress to senior leadership and non-technical stakeholders.

• Lead and manage a team of machine learning engineers and applied scientists.

• Carry out additional job-related duties as assigned.

⛳️ Requirements

• A Bachelor's degree in Computer Science, Data Science, Engineering, or a related field is required.

• Equivalent years of relevant work experience may be accepted in place of the required education.

• A minimum of eight (8) years of software/ML engineering experience is required.

• At least five (5) years of leadership experience is necessary.

• Proven experience in productionizing ML models at scale, including CI/CD, model versioning, monitoring, and retraining pipelines.

• Background in regulated, PHI-governed environments is essential.

• Familiarity with HIPAA and healthcare data standards is required.

• Knowledge of health plan, payer, or healthcare provider environments is preferred.

• Exposure to HEDIS/Stars, HCC risk adjustment, claims (837/835), and standards such as HL7, FHIR, and CCDA.

• Proficiency in cloud infrastructure, ideally Azure, and Infrastructure-as-Code practices.

• Hands-on experience with Databricks or similar lakehouse platforms, Delta Lake, MLflow, and Spark.

• Strong skills in Python and SQL.

• Understanding of supervised and unsupervised learning, model evaluation, and feature engineering.

• Familiarity with current AI/LLM concepts like RAG, embeddings, prompt engineering, and generative model evaluation.

• Ability to assess or implement knowledge graph, entity resolution, or Member 360 initiatives.

• Capability to present model risk or AI governance documentation to regulators, auditors, or compliance committees.

• Excellent communication, service orientation, consulting, leadership, and management skills.

• Capacity to work collaboratively with all levels of management.

• Skill in managing multiple complex priorities in a dynamic environment.

• Knowledge of outsourcing and staff augmentation strategies that support testing processes.

• Understanding of the managed care industry is preferred.

• Licensure and certification: None required.

• Willingness to travel as needed for business purposes.

🏝️ Benefits

• Competitive base salary ranging from $135,600.00 to $237,400.00.

• Potential bonus linked to both company and individual performance.

• Comprehensive and substantial total rewards package offered.

• Complete support for overall well-being.