Lead Software Architect — AI Platform Engineering

General Dynamics is a leading provider of high technology solutions, products, and services for defense and scientific missions. They are seeking a Lead Software Architect to define the technical vision for their AI platform, oversee the tech stack, and ensure production reliability while working closely with various teams to deliver working software.

Responsibilities:

• You will define the technical vision for the AI platform — its service boundaries, data flows, integration contracts, and deployment topology
• You will select, evaluate, and when necessary replace the tools, frameworks, and services your team depends on
• You will write code
• You will review code
• You will debug production systems at the point of failure
• You will set the engineering standards for the platform team — CI/CD practices, testing expectations, observability requirements, operational toil automation, and documentation norms
• You own the operational health of the platform — SLOs, incident response, capacity planning, and the automation that keeps systems self-healing at 2 AM so your team doesn't have to
• You will work directly with infrastructure, security, cloud, data, and ML pipeline teams — and you'll be credible in every one of those conversations because you've done the work yourself
• You will define milestones, manage technical risk, and ensure the team delivers working software on a predictable cadence

Requirements:

• Bachelor's degree in Engineering, plus a minimum of 10 years of relevant experience; or Master's degree in Engineering, plus a minimum of 8 years of relevant experience
• 10+ years of software engineering experience, with at least 4 years in a technical leadership or architecture role
• You have personally architected and shipped production platforms — not advised on them, not managed teams that built them. You built them
• Deep experience with cloud-native architectures (AWS, Azure, or GCP), including container orchestration, API gateway design, event-driven systems, and infrastructure as code
• Extensive production AI/ML systems experience — model serving, LLM integration, prompt management, or AI pipeline orchestration. You understand the difference between a demo and a production AI system
• Demonstrated technical depth across multiple domains — not just application code, but infrastructure, networking, data engineering, ML pipelines, security, or platform tooling. You're the person who debugs a Kubernetes networking issue in the morning and reviews a model-serving architecture in the afternoon
• You built your career at commercial tech companies — product engineering, SaaS platforms, cloud infrastructure, or startups that had to ship or die. You know what good engineering looks like at speed, and you're ready to bring that standard somewhere it's needed
• Experience with LLM application development — RAG pipelines, embedding strategies, fine-tuning workflows, or multi-model orchestration
• Familiarity with AI-assisted development tools and practices (Copilot, Cursor, Claude Code, or similar) and a perspective on how they should be integrated into professional engineering workflows
• Experience operating in multi-cloud or hybrid-cloud environments with complex networking and security requirements
• A track record of mentoring senior engineers and building high-performing platform teams
• You have never worked in defense or government — and you see that as a strength, not a gap. We do too