Senior Manager AI Engineer

CVS Health•New York, NY

1d•$106,605 - $260,590

About The Position

The Senior Manager of AI Engineering is responsible for establishing and leading the organization's AI engineering capabilities, driving the design, development, and operationalization of enterprise-scale AI and Generative AI solutions. This leader combines deep technical expertise with strong people leadership to build high-performing engineering teams, define architectural standards, and accelerate the delivery of secure, scalable, and compliant AI platforms. The ideal candidate is an experienced technical leader who remains hands-on with modern AI technologies, cloud-native architectures, agentic frameworks, and production AI systems. This individual will serve as the organization's technical authority for AI engineering, partnering with business and technology leaders to transform strategic opportunities into measurable business outcomes.

Requirements

7+ years of software engineering, platform engineering, machine learning, or AI engineering experience.
2+ years leading engineering teams or architecture initiatives, serving as a technical lead, solution architect, principal engineer, or engineering manager for enterprise-scale platforms.
Proven experience designing, building, and operating cloud-native applications on AWS and/or GCP.
Experience delivering production AI solutions, including Generative AI, Agentic AI, and Retrieval-Augmented Generation (RAG) platforms.
Strong understanding of distributed systems, APIs, microservices, event-driven architectures, and modern software engineering practices.
Experience partnering within a product operating model to define technology roadmaps and deliver business outcomes.
Exceptional communication, mentorship, and technical leadership skills with the ability to influence both technical and executive audiences.
Bachelor’s Degree in Computer Science or a related field, or equivalent experience.

Nice To Haves

Experience operating within Agile, Scrum, and/or SAFe delivery frameworks in large enterprise environments.
Health Care Industry experience preferred.

Responsibilities

Build, lead, mentor, and develop a high-performing team of AI and software engineers while fostering a culture of innovation, accountability, and continuous learning.
Establish the technical vision, engineering standards, and architectural roadmap for enterprise AI platforms and solutions.
Serve as the senior technical leader for AI initiatives, providing architectural guidance, conducting design reviews, and driving key engineering decisions.
Coach and develop engineers in system design, software engineering best practices, operational excellence, and professional growth.
Partner with executive leadership to align AI investments, technology strategy, and delivery priorities with business objectives.
Define and execute the strategy for enterprise AI, Generative AI, Agentic AI, and intelligent automation initiatives.
Lead end-to-end solution delivery across the software development lifecycle, ensuring solutions meet business, performance, scalability, reliability, security, and cost objectives.
Evaluate emerging AI technologies, platforms, and industry trends, recommending adoption strategies and architectural standards.
Lead production support, incident response, and operational risk management for critical AI platforms and applications.
Provide technical leadership for cloud-native AI platforms and applications across AWS, GCP, and enterprise ecosystems.
Guide engineering teams in the design and implementation of: LLM and Generative AI solutions, Retrieval-Augmented Generation (RAG) architectures, Agentic AI and workflow orchestration platforms, Serverless and event-driven architectures, APIs, microservices, and platform services, AI observability, evaluation, and monitoring frameworks.
Establish reusable engineering patterns, frameworks, and platform accelerators that improve scalability, reliability, and delivery velocity.
Ensure AI solutions align with Responsible AI principles, enterprise technology standards, security controls, and regulatory requirements.
Partner with security, privacy, legal, compliance, and risk teams to establish governance frameworks for AI development and deployment.
Promote best practices for model evaluation, explainability, auditability, data protection, and operational risk management.
Partner with product, engineering, data, security, compliance, and business stakeholders to define roadmaps and prioritize initiatives.
Communicate complex technical concepts effectively to executive leadership, technical teams, and business partners.
Serve as a trusted advisor and thought leader on enterprise AI strategy and adoption.