Enterprise AI Engineering & Platforms Manager

General Dynamics Electric Boat•Groton, CT

About The Position

Electric Boat is accelerating its deployment and adoption of artificial intelligence to transform how it operates. The Enterprise AI Engineering & Platforms Manager is a deeply technical leader who connects the possibilities of AI with the practical work required to deploy, integrate, secure, operate, and scale it in a large enterprise. The role helps define enterprise AI technology direction and contributes hands-on across architecture, infrastructure, platforms, integration, security, data, deployment, and operations. It also drives platform governance, cost and lifecycle management, tool selection, and enterprise alignment to support consistent, trusted, and responsible AI adoption. The manager contributes to enterprise AI strategy and roadmap, architects the enterprise AI foundation, and leads engineering of reusable AI platforms and services. The role evaluates rapidly changing technologies and establishes practical standards and guardrails. It also champions enterprise-wide cultural transformation and AI adoption, and attracts, develops, and leads engineers and architects while fostering responsible innovation, engineering rigor, and delivery.

Requirements

Bachelor of Science degree in Computer Science, Engineering, Information Systems, or Data Science.
At least 8 years of experience with cloud platforms, enterprise infrastructure, APIs, distributed systems, data platforms, networking, identity, security, and observability.
At least 4 years of supervisory experience.
Demonstrated experience building and deploying AI, machine learning, or large language model solutions into production, including taking solutions from experimentation through deployment and ongoing operations.
Experience leading enterprise-scale technology initiatives.
Experience leading highly technical engineering or architecture teams.
Experience working in Department of War (DoW) or Defense Industrial Base (DIB) security environments.
Strong communication skills and ability to explain highly technical concepts to engineers and senior business leaders.
Strong understanding of the infrastructure required to run AI in a large enterprise environment.

Nice To Haves

Deep understanding of current AI platforms, models, frameworks, architectures, and capabilities.
Strong understanding of data architecture and the role of high-quality enterprise data in successful AI implementations.

Responsibilities

Contribute to the enterprise AI strategy and roadmap for becoming an AI-enabled enterprise.
Translate business needs into scalable AI platform requirements.
Define and support implementation of enterprise AI platforms, infrastructure, architecture, and engineering capabilities, including compute, cloud, networking, storage, data, identity, security, observability, and operations.
Develop patterns for using foundation models, agentic systems, retrieval-augmented generation (RAG), AI applications, machine learning, and other emerging technologies in the enterprise environment.
Develop practical approaches for integrating modern AI capabilities into a large legacy environment without requiring the entire technology ecosystem to be rebuilt first.
Establish reusable platforms, services, APIs, tools, and deployment patterns that enable teams to build and consume AI capabilities safely and efficiently.
Track developments in AI models, agent frameworks, AI infrastructure, inference technologies, developer tools, and the broader AI ecosystem; evaluate emerging technologies for business value.
Establish enterprise patterns for model selection, security, data usage, deployment, governance, monitoring, and lifecycle management.
Develop AI-related standards and best practices.
Champion and drive AI adoption across the enterprise.
Attract, develop, and lead engineers and architects, fostering responsible innovation, engineering rigor, and delivery.
Drive platform governance, cost management, lifecycle management, tool selection, and enterprise alignment.