Senior Manager- AI Engineer

CVS Health•Work At Home-Connecticut, CT

2d•$130,295 - $260,590

About The Position

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.

Requirements

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)

Nice To Haves

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

Responsibilities

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
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
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
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