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.

• The platform architecture. You will define the technical vision for the AI platform — its service boundaries, data flows, integration contracts, and deployment topology. This is hands-on architecture work, not from a whiteboard, but from working code and real operational feedback.
• The tech stack. You will select, evaluate, and when necessary replace the tools, frameworks, and services your team depends on. You own the technical direction and are expected to defend it with evidence, not authority.
• Production code. You will write code. You will review code. You will debug production systems at the point of failure. This is not a management role that occasionally looks at a pull request.
• Engineering culture. You will set the engineering standards for the platform team — CI/CD practices, testing expectations, observability requirements, operational toil automation, and documentation norms. You will model the standard, not just write it down.
• Production reliability. 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 treat production as a product, not an afterthought.
• Technical decisions across organizational boundaries. 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, not just managed people who have. Some of those relationships haven't been tested before. You will make them work.
• You are responsible for translating architectural intent into shipped capability. You will define milestones, manage technical risk, and ensure the team delivers working software on a predictable cadence.

The following are non-negotiable. If you do not meet these, this is not the right role.

• 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.
• Extensvie 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.

These are not requirements, but they will make us pay attention:

• 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.

If you are coming from a commercial tech environment, you may assume that defense engineering means slow releases, change boards, and architecture by committee. This team does not work that way. We built it specifically so it wouldn't. Here is what is different:

• The architecture decision rights belong to this team — not a committee, not IT, not a governance board.
• You will operate across the full technical stack — infrastructure, data, ML, security, cloud — and be expected to go deep in any of them on any given day.
• The problems are real and they span domains: multi-model AI in production, agentic workflows at enterprise scale, and the operational discipline to keep it all running in environments where downtime isn't an option.
• AI-native development tools are the standard, not a side experiment.

We are looking for someone who:

• Has strong opinions, loosely held — and can change direction when the evidence says so.
• Writes clearly and communicates technical decisions to non-technical stakeholders without dumbing them down.
• Has built and led teams where engineers want to stay — not because of perks, but because of the work.
• Treats AI as an engineering discipline, not a novelty.
• Has been the person who gets pulled into problems outside your job description — because you understand systems end to end, not just your slice of them.
• Is genuinely curious about how things work beneath the abstraction layer, and that curiosity has made you dangerous in more than one domain.
• Will challenge how things have always been done — and has the credibility and conviction to make the better way stick.