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Director, Model Engineering – Operations
CareSourceCareSource director leading Databricks AI/ML platforms and MLOps for regulated health plan operations. Guiding model production, governance, and engineering teams.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading the development and optimization of AI and machine learning solutions within healthcare, ensuring compliance with HIPAA and other regulatory standards. Proficient in managing the end-to-end MLOps lifecycle on Databricks, with strong capabilities in model productionization, monitoring, and team leadership.
Highest-signal resume keywords
MLOps Lifecycle ManagementDatabricks ProficiencyAI/ML Model ProductionizationHIPAA Compliance KnowledgeLeadership in Machine Learning
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonSQLMachine Learning EngineeringFeature EngineeringModel EvaluationCI/CD PipelinesSupervised LearningUnsupervised LearningGenerative AI ConceptsCloud Infrastructure
Soft Skills
Strong CommunicationLeadershipService OrientationConsultingCollaboration
Tools & Technologies
DatabricksDelta LakeMLflowSparkAzure
Industry Keywords
Healthcare Data StandardsHEDIS/StarsRisk AdjustmentClaims (837/835)HL7FHIRCCDAManaged Care IndustryPHI-Governed EnvironmentsModel Risk Documentation
Tech Stack
Tools & technologiesAzureCloudPythonSparkSQL
About the role
Key responsibilities & impact- Lead the strategy, development, implementation, and optimization of enterprise AI and machine learning solutions supporting health plan operations and business objectives
- Lead design, development, and productionization of ML and AI models across risk adjustment, quality measures, care management, utilization management, fraud/waste/abuse, and member/provider experience use cases
- Own the end-to-end MLOps lifecycle on Databricks, including feature engineering, feature stores, model training and versioning, CI/CD, deployment, and automated retraining
- Establish model monitoring practices for drift, performance degradation, bias/fairness, and champion-challenger frameworks
- Guide responsible adoption of generative AI and LLM capabilities for analytics, member/provider tools, and operational automation
- Define engineering standards, design patterns, and reusable ML components
- Partner with governance and security teams to ensure HIPAA, CMS, and NCQA compliance
- Manage model risk documentation and validation for regulatory review, audits, and quality submissions
- Own cost, performance, and reliability of the Databricks ML platform
- Collaborate with BI, data engineering, and data science teams on canonical data models and lakehouse architecture
- Translate clinical, quality, finance, and operations problems into scoped ML engineering initiatives with success metrics and timelines
- Communicate technical strategy, risk, and progress to senior leadership and non-technical stakeholders
- Lead and oversee a team of machine learning engineers and applied scientists
- Perform other job-related duties as requested
Requirements
What you’ll need- Bachelor's degree in Computer Science, Data Science, Engineering, or related field required
- Equivalent years of relevant work experience may be accepted in lieu of required education
- Eight (8) years of software/ML engineering experience required
- Five (5) years of leadership experience required
- Experience productionizing ML models at scale, including CI/CD, model versioning, monitoring, and retraining pipelines
- Experience in regulated, PHI-governed environments
- Working knowledge of HIPAA and healthcare data standards
- Familiarity with health plan, payer, or healthcare provider environments
- Exposure to HEDIS/Stars, HCC risk adjustment, claims (837/835), and HL7, FHIR, and CCDA standards
- Knowledge of cloud infrastructure, preferably Azure, and Infrastructure-as-Code practices
- Hands-on proficiency with Databricks or comparable lakehouse platforms, Delta Lake, MLflow, and Spark
- Strong Python and SQL skills
- Understanding of supervised and unsupervised learning, model evaluation, and feature engineering
- Knowledge of modern AI/LLM concepts including RAG, embeddings, prompt engineering, and generative model evaluation
- Ability to evaluate or implement knowledge graph, entity resolution, or Member 360 initiatives
- Ability to present model risk or AI governance documentation to regulators, auditors, or compliance committees
- Strong communication, service orientation, consulting, leadership, and management skills
- Ability to work collaboratively with all levels of management
- Ability to manage multiple complex priorities in a changing environment
- Knowledge of outsourcing and staff augmentation strategies supporting testing processes
- Knowledge of the managed care industry preferred
- Licensure and certification: None
- Ability to travel as required by business needs
Benefits
Comp & perks- Base salary of $135,600.00–$237,400.00
- Bonus tied to company and individual performance may be available
- Substantial and comprehensive total rewards package
- Total well-being support