Head of Data and AI Platforms

Job Summary

The Head of Data & AI Platforms leads the engineering organization responsible for building and operating the foundation that enables AI to transform healthcare at Medline. This role owns the enterprise data and AI platforms that power how the company generates, scales, and operationalizes insight and intelligence. This is a deeply technical leadership role focused on taking an already strong data platform to the next level—evolving it into a unified, production-grade Data + AI platform that supports modern AI, including machine learning, generative AI, and emerging agentic patterns at enterprise scale. The role ensures that every AI and data product at Medline is built on a reliable, scalable, and reusable foundation—driving speed, quality, and consistency across the enterprise.

Major Responsibilities

Platform Ownership

• Own the enterprise Data and AI Platforms, including the roadmap, architecture, and engineering best practices.
• Evolve Medline’s existing data platform into a unified Data + AI platform that supports ML, GenAI, and emerging AI application patterns.
• Establish the platform as a shared, reusable foundation that scales across all domains and use cases.
• Modernize and extend the platform to support real-time data, large-scale model inference, and next-generation AI workloads.

Data, AI & MLOps Engineering Leadership

• Lead Data Engineering, ML/AI Platform (MLOps), and AI Platform engineering as a single, high-performing organization.
• Build and scale a world-class engineering team with strong depth in distributed systems, data platforms, and production AI.

Standards, Quality & Production Readiness

• Define and enforce engineering standards, platform guardrails, and production readiness criteria.
• Ensure the platform is secure, reliable, and cost-efficient by design.
• Establish SLAs and operational excellence practices to run the platform as a high-availability service.

Operating Model & Ways of Working

• Design the platform to be the fastest and easiest path for teams to build and deploy data and AI products.
• Drive adoption through strong developer experience, tooling, and self-service capabilities.
• Establish clear onboarding pathways and support models for enterprise teams.

Outcomes & Operational Excellence

• Improve time-to-production for AI and data products across the enterprise.
• Increase the number and scale of models and data products running reliably in production.
• Continuously optimize platform cost, performance, and developer productivity.

Platform Run / Operations

• Manage teams that monitor the platform and be accountable for monitoring, service levels on availability, and incident management.
• Establish SLAs, availability standards, monitoring requirements, and operational governance.
• Provide executive oversight of major incidents, coordinated recovery, and corrective actions that reduce recurring issues and drive resolutions through automation, self-service and knowledge management.

Minimum Job Requirements

Education: Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field. Certification / Licensure: No specific certification or licensure required. Work Experience: 15+ years of experience in engineering leadership across data platforms, distributed systems, or AI/ML infrastructure. Proven track record of building and scaling enterprise-grade data or AI platforms.

Knowledge / Skills / Abilities

• Deep hands-on understanding of modern cloud data platforms (Databricks, Microsoft Fabric, or equivalent).
• Strong experience with MLOps and production AI systems (model serving, feature stores, monitoring, lifecycle management).
• Demonstrated ability to lead engineering teams and deliver highly scalable, reliable platforms.
• Strong platform-as-a-product mindset — shared services, SLAs, and reuse over rebuild.
• Strong bias for outcomes — platform uptime, cost, and time-to-production over artifacts.

Preferred Job Requirements

Education: Master’s degree in Computer Science, Engineering, or a related technical field. Certification / Licensure: Relevant cloud, data platform (Databricks / Azure), or MLOps credentials. Work Experience: Prior experience leading large remote / India / offshore engineering teams. Experience delivering an enterprise-wide Data + AI platform from the ground up.

Preferred Knowledge / Skills / Abilities

• Experience evolving an existing data platform into a modern Data + AI platform.
• Familiarity with GenAI, LLM platforms, and emerging agentic architectures.
• Experience operating shared platforms with SLAs and internal customer models.
• Experience leading global engineering teams, including offshore models.
• Strong track record of improving engineering velocity and platform adoption at scale.
• Familiarity with Azure Data Factory, Azure Data Lake, Databricks, Azure Synapse, Microsoft Fabric and/or Power BI is preferred.