Director, Model Engineering & Operations

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Director, Model Engineering & Operations based in United States.

This leadership role offers the opportunity to shape the future of AI and machine learning capabilities within a complex healthcare environment. You will lead the strategy, development, and operationalization of scalable ML platforms that support critical business and clinical initiatives. Working at the intersection of engineering, data science, governance, and healthcare operations, you will transform advanced analytics into reliable solutions with measurable impact. The role combines technical leadership, team development, and strategic execution to drive innovation across enterprise AI initiatives. You will oversee the complete machine learning lifecycle, from model development and deployment to monitoring, optimization, and compliance. This position is ideal for an experienced AI leader passionate about building responsible, secure, and high-performing ML systems that improve healthcare outcomes.

Accountabilities:: The Director, Model Engineering & Operations will lead enterprise AI and machine learning engineering strategies, ensuring the successful delivery, scalability, and governance of production-grade ML solutions. This role requires strong technical expertise, business acumen, and leadership skills to align AI capabilities with organizational goals while maintaining compliance and operational excellence.

Lead the design, development, deployment, and optimization of AI and machine learning solutions supporting healthcare business functions such as risk adjustment, quality measurement, care management, utilization management, fraud prevention, and member experience.

Own the end-to-end MLOps lifecycle, including feature engineering, feature store design, model training, versioning, CI/CD pipelines, deployment strategies, automated retraining, and production operations.

Establish and maintain model monitoring frameworks, including drift detection, performance tracking, fairness evaluations, and champion-challenger approaches.

Guide the responsible adoption of generative AI and large language model capabilities for analytics, automation, and customer-facing solutions.

Define engineering standards, reusable components, architecture patterns, and best practices to accelerate AI delivery across teams.

Partner with security, governance, and compliance teams to ensure AI systems meet healthcare regulatory requirements, including data privacy, auditability, and responsible AI standards.

Manage model risk documentation, validation processes, and regulatory readiness in partnership with compliance and quality teams.

Optimize the performance, reliability, and cost efficiency of the ML platform ecosystem, including infrastructure design, compute optimization, and technology evaluations.

Collaborate with data engineering, analytics, and business teams to align ML pipelines with enterprise data strategies and architecture.

Translate complex business challenges into clearly defined AI/ML initiatives with measurable outcomes, timelines, and success criteria.

Communicate technical strategies, risks, and progress effectively to executives and non-technical stakeholders.

Lead, mentor, and develop machine learning engineers and applied scientists while fostering a collaborative, high-performing team culture.

Requirements:

The ideal candidate is an experienced AI and machine learning leader with a strong background in production ML systems, MLOps, and healthcare data environments. This role requires a combination of technical depth, strategic thinking, and leadership capability to successfully drive enterprise-scale AI transformation.

Bachelor’s degree in Computer Science, Data Science, Engineering, or a related technical field required.

Master’s degree in Computer Science, Data Science, Engineering, or a related discipline preferred.

Eight (8)+ years of experience in software engineering, machine learning engineering, or related technical fields.

Five (5)+ years of leadership experience managing technical teams.

Proven experience productionizing machine learning models at scale, including CI/CD, model versioning, monitoring, and automated retraining processes.

Experience working with regulated healthcare or PHI-governed environments, including knowledge of HIPAA and healthcare data standards.

Hands-on experience with Databricks or comparable lakehouse platforms, Delta Lake, MLflow, Spark, Python, and SQL.

Strong understanding of machine learning concepts, including supervised and unsupervised learning, model evaluation, feature engineering, and AI system design.

Knowledge of modern AI and LLM concepts, including retrieval-augmented generation, embeddings, prompt engineering, and generative AI evaluation.

Familiarity with healthcare data ecosystems, including HEDIS/Stars, HCC risk adjustment, claims data, HL7, FHIR, or related standards is a plus.

Experience with cloud infrastructure, preferably Azure, and Infrastructure-as-Code practices.

Ability to influence technical teams, executive stakeholders, regulators, and compliance partners through strong communication skills.

Strong leadership, mentoring, and organizational skills with the ability to manage multiple complex priorities.

Experience with AI governance, model risk documentation, and regulatory presentations preferred.

Strong consulting mindset, collaboration skills, and ability to operate effectively in a changing environment.

Benefits:

Competitive salary range of $135,600 to $237,400 annually , based on experience, qualifications, and role scope.

Potential eligibility for performance-based bonus opportunities.

Comprehensive total rewards package supporting employees’ overall well-being.

Opportunity to lead impactful AI and machine learning initiatives within a mission-driven healthcare environment.

Remote work flexibility with opportunities to collaborate across teams.

Professional growth opportunities through leadership development and continuous learning.

Opportunity to work with advanced technologies including AI platforms, machine learning systems, and modern data architectures.

Collaborative culture focused on innovation, partnership, and operational excellence.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1