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AI is no longer a specialized toolset—it is a foundational enterprise capability. As the Vice President of AI Platform, you are forward-thinking and execution obsessed. You will build, operate, and continuously evolve the company’s enterprise AI platform. This leader will be responsible for establishing a reusable, extensible, and secure foundation for all AI development—spanning traditional machine learning, generative AI, and agentic AI.
The VP of AI Platform will empower developers, data scientists, and business units with tools and infrastructure that make AI innovation fast, safe, and scalable across the enterprise. This is a platform leadership role that sits at the core of Humana’s long-term technology strategy.

               

Key Responsibilities

• Build the Enterprise AI Platform
  Architect a secure, modular, cloud-native AI platform that supports the full lifecycle of AI development—from data ingestion to model deployment and integration. Prioritize reusable components such as feature stores, model registries, vector databases, embedding libraries, RAG pipelines, agent frameworks, and fine-tuning workflows.

• Design for Reusability and Extensibility
  Ensure the platform enables composability and cross-team reuse through well-defined APIs, standard schemas, common solution patterns, and plug-in interfaces. Architect for rapid integration of future AI capabilities without costly refactoring.

• Operate at Scale with Reliability and Security
  Lead the operational support of the platform as a high-availability, multi-tenant service. Ensure enterprise-grade SLAs, robust observability, failover resilience, and built-in security controls. Optimize for performance and cost across diverse AI workloads—including LLM inference, agentic workflows, and batch model training.

• Deliver World-Class Developer and Data Scientist Experience
  Provide intuitive APIs, self-service tools, and seamless CI/CD integration that streamline experimentation, deployment, and monitoring. Prioritize developer productivity, data scientist autonomy, and frictionless onboarding as critical platform KPIs.

• Establish Governance and Responsible AI Guardrails
  Build native support for model lineage tracking, version control, drift detection, audit logging, and compliance automation. Align platform policies with responsible AI standards and regulatory frameworks. Embed safety, fairness, transparency, and explainability into the platform’s foundation.

• Continuously Evolve with the AI Ecosystem
  Lead the evaluation and responsible adoption of emerging technologies—including open-source LLMs, synthetic data platforms, memory-augmented agents, and privacy-preserving machine learning. Maintain a forward-compatible architecture that allows rapid exploration without destabilizing production systems.

• Drive Adoption and Strategic Alignment Across the Enterprise
  Collaborate across the enterprise to ensure platform capabilities align with real-world use cases. Act as a multiplier by promoting standards, fostering internal communities of practice, and scaling success patterns across the enterprise.

Use your skills to make an impact
 

Required Qualifications:

• Master’s degree in computer science, Machine Learning, or a related quantitative discipline.

• 10+ years leading platform teams focused on ML or AI in large enterprise.

• Proven track record in building enterprise-grade AI platforms at scale, with deep understanding of machine learning infrastructure, distributed model training, LLMOps, agentic architectures, and real-time inference systems.

• Strong cloud-native engineering background (AWS, Azure, and GCP), with expertise in Kubernetes, containerization, and modern DevSecOps practices.

• Demonstrated experience aligning AI platform strategy with enterprise business goals and delivering measurable business value.

• Demonstrated success enabling high developer productivity and reusable AI assets across complex, regulated environments.

• Demonstrated ability to drive platform adoption and cultural change in a complex, matrixed organization.

• Exceptional oral and written communication skills

Preferred

• Doctorate degree in Computer Science, Machine Learning, or a related quantitative discipline.

• Experience in highly regulated industries such as healthcare, life sciences, or financial services, where reliability, safety,