Job Title

Head of AI Engineering and Strategy

Job Summary

Aetna Medicare Product Tools and Technology is seeking a Head of AI Engineering and Strategy to define and lead the vision for enterprise AI capabilities within the engineering organization. This role is responsible for establishing the platforms, infrastructure, and operating model required to design, build, and scale AI driven applications across the enterprise.

This position combines senior engineering leadership with strategic oversight, driving the adoption of modern AI and ML capabilities, including large language models (LLMs), conversational voice AI, AI augmented business intelligence, and chatbot solutions, into mission critical systems and workflows.

You will operate as a key member of the technology leadership team, partnering with engineering, product, analytics, and business executives to shape AI strategy, influence enterprise investment decisions, and drive organization wide adoption of AI capabilities that enable intelligent automation and long-term business transformation.

Key Responsibilities

AI / ML Engineering

Define the technical vision and architecture for AI and ML solutions, including LLM based applications, conversational voice AI, chatbot platforms, and AI augmented BI systems

Oversee development of training data pipelines and datasets for fine tuning, evaluation, and inference at enterprise scale

Establish enterprise standards for model performance monitoring, drift detection, retraining, and lifecycle governance

Drive implementation of vector embeddings, semantic search, and AI orchestration frameworks

Application Development

Provide engineering leadership for backend services (APIs, microservices) enabling scalable AI capabilities across the enterprise

Oversee development of scalable data pipelines supporting ingestion, transformation, and real time inference workloads

Drive integration of AI capabilities into enterprise platforms, including customer facing voice and chat systems and internal analytics environments

Ensure solutions meet enterprise standards for scalability, reliability, performance, and security

MLOps and Platform Engineering

Define and govern model lifecycle management practices including versioning, deployment, rollback, and compliance

Lead the development of enterprise AI platforms and infrastructure for model hosting, orchestration, and scaling

Establish CI and CD standards and deployment frameworks for AI systems across engineering teams

Build and oversee observability layers to monitor system performance, model behavior, and operational health

Set direction for AI safety and responsible AI practices, including guardrails for bias mitigation, hallucination reduction, and policy adherence

Collaboration and Strategy

Set and drive enterprise AI strategy aligned to technology vision, platform evolution, and long-term organizational priorities

Lead alignment across a highly matrixed organization, influencing engineering, product, analytics, and business leadership

Serve as a trusted advisor to executive leadership, communicating AI strategy, technical trade-offs, risks, and business impact

Own AI investment strategy, including prioritization, funding alignment, and resource allocation across initiatives

Drive enterprise-wide AI adoption by establishing scalable enablement models across engineering and business teams

Define and execute capability uplift strategies, including upskilling engineers, promoting best practices, and enabling self-service AI development

Champion innovation by introducing emerging AI technologies, tools, and solution patterns to accelerate experimentation and delivery

Establish and govern AI vendor and partner strategy, including evaluation, selection, negotiation, and performance oversight

Oversee SOW development and partner with product and finance leadership to manage budgets, forecasts, and investment planning

Act as the primary interface between engineering and executive leadership, ensuring transparency, accountability, and delivery outcomes

Influence enterprise architecture, engineering standards, and AI governance frameworks

Required Qualifications

Extensive experience leading engineering or AI and ML organizations within large scale enterprise environments

Demonstrated ability to operate at a senior leadership level, influencing executive stakeholders and enterprise strategy

Proven experience owning or driving technology investment strategy, budgeting, and resource allocation

Experience leading transformation initiatives and driving adoption of emerging technologies across organizations

Experience building and scaling engineering platforms, systems, or organizational capabilities

Experience within healthcare, health insurance, or regulated healthcare environments, with strong understanding of compliance, data privacy, and domain specific challenges

Deep expertise in software engineering fundamentals (SDLC, architecture, distributed systems design)

Proficiency in one or more programming languages (Python, C#, Java, etc.)

Experience building data pipelines and working with structured and unstructured data

Hands on experience with AI and ML frameworks, platforms, or applied AI systems

Strong understanding of APIs, microservices, and cloud-based architectures

Experience with cloud platforms (Azure, AWS, or GCP)

Familiarity with databases (SQL / NoSQL)

Experience leading vendor strategy, including evaluation, selection, and delivery governance

Preferred Qualifications

Experience defining or leading enterprise AI strategy or platforms at scale

Hands on experience with LLMs, prompt engineering, or fine-tuning models

Experience building conversational AI (voice and chat) ecosystems

Experience with AI augmented analytics or business intelligence platforms

Experience with vector databases, embeddings, and semantic search

Familiarity with MLOps, observability, and model monitoring frameworks

Experience implementing responsible AI, governance, and risk management practices

Experience operating at Director or VP level or equivalent leadership scope

Experience in healthcare, analytics, or enterprise data platforms

Exposure to tools such as Databricks, Spark, or real time analytics systems

Technology Stack

AI/ML: LLMs), conversational AI (voice & chat via Kore.ai), embeddings, fine-tuning, model evaluation

Languages: Python, C#, Java

Cloud: Azure OpenAI, Microsoft Copilot ecosystem, AWS, GCP Vertex AI

Data: Snowflake, SQL Server, Databricks, Spark, Hadoop

DevOps: Docker, CI/CD pipelines

Observability & Safety: Model monitoring, logging, guardrails, policy enforcement frameworks

Frontend: React-based AI interfaces

Pay Range

The typical pay range for this role is:

$130,295.00 - $260,590.00

This pay range represents the base hourly rate or base annual full-time salary for all positions in the job grade within which this position falls. The actual base salary offer will depend on a variety of factors including experience, education, geography and other relevant factors. This position is eligible for a CVS Health bonus, commission or short-term incentive program in addition to the base pay range listed above. This position also includes an award target in the company's equity award program.

Great benefits for great people

We take pride in offering a comprehensive and competitive mix of pay and benefits that reflects our commitment to our colleagues and their families.

This full-time position is eligible for a comprehensive benefits package designed to support the physical, emotional, and financial well-being of colleagues and their families. The benefits for this position include medical, dental, and vision coverage, paid time off, retirement savings options, wellness programs, and other resources, based on eligibility.

Additional details about available benefits are provided during the application process and on Benefits Moments.

We anticipate the application window for this opening will close on: 06/30/2026