Enterprise AI Engineering & Platforms Manager

Overview

Electric Boat is accelerating its deployment and adoption of artificial intelligence with a tremendous opportunity to use modern technology and AI to transform how we operate. To support this transformation, we are seeking an Enterprise AI Engineering & Platforms Manager — a deeply technical leader who can bridge the gap between what is possible with AI today and what can actually be deployed, integrated, secured, operated, and scaled in a large enterprise.

We are looking for a hands-on technology leader who understands AI, stays current with the rapidly evolving AI landscape, and knows how to make practical technology decisions. The successful candidate will help define our enterprise AI technology direction while also getting into the details of architecture, infrastructure, platforms, integration, security, data, deployment, and operations. This position will also drive platform governance, cost management, lifecycle management, tool selection, and enterprise alignment to ensure consistency, trust, and responsible adoption of AI.

You will also play a critical role in cultural transformation — partnering across the enterprise to enable broad adoption of new AI technologies.

Key Responsibilities

Architecture & Strategic Leadership. Contribute to enterprise AI strategy and roadmap to become an AI-enabled enterprise. Translate business needs into scalable AI platform requirements.

Architect the enterprise AI foundation. Define and support implementation of the platforms, infrastructure, architecture, and engineering capabilities required to deploy AI at enterprise scale. This includes the compute, cloud, networking, storage, data, identity, security, observability, and operational capabilities required to support AI workloads. Develop patterns for using foundation models, agentic systems, RAG, AI applications, machine learning, and other emerging technologies within our enterprise environment.

Understand the realities of a large legacy environment and develop practical approaches for integrating modern AI capabilities without requiring the entire technology ecosystem to be rebuilt first.

Lead AI platform and service engineering. Establish reusable platforms, services, APIs, tooling, and deployment patterns that enable teams across the business to build and consume AI capabilities safely and efficiently.

Evaluate rapidly changing technology. Stay current on developments in AI models, agent frameworks, AI infrastructure, inference technologies, developer tools, and the broader AI ecosystem. Separate meaningful advances from hype. Evaluate emerging AI technologies and determine where and how they can create real business value

Create technology standards and guardrails. Establish practical enterprise patterns for model selection, security, data usage, deployment, governance, monitoring, and lifecycle management. Develop AI-related standards and best practices.

Enterprise Cultural Transformation. Champion and drive AI adoption across the enterprise.

Team Leadership and Talent Development. Attract, develop, and lead highly capable engineers and architects while creating a culture of responsible innovation, engineering rigor, and delivery.

Required

• Bachelor’s of Science degree in Computer Science, Engineering, Information Systems, or Data Science
• 8+ years of experience with cloud platforms, enterprise infrastructure, APIs, distributed systems, data platforms, networking, identity, security, and observability.
• 4+ years of supervisory experience
• Demonstrated experience building and deploying AI/ML/LLM solutions into production. Experience taking AI or machine learning solutions from experimentation through production deployment and ongoing operations.
• Experience leading enterprise-scale technology initiatives.
• Experience leading highly technical engineering or architecture teams.
• Experience working inside Department of War (DoW) or Defense Industrial Base (DIB) security environments

Preferred

Deep understanding of the latest AI platforms, models, frameworks, and AI architectures and what they can do.

A strong understanding of data architecture and the role of high-quality enterprise data in successful AI implementations.

Skills

Strong communication skills and the ability to explain highly technical concepts to both engineers and senior business leaders.

Strong understanding of the infrastructure required to run AI in a large enterprise environment.