Applied AI Lead - Infrastructure & Deployment

Select how often (in days) to receive an alert:

Location:

Beavercreek, OH, US, 45431
Fort Walton Beach, FL, US, 32548
Frederick, MD, US, 21703

Job

The Leonardo DRS Airborne and Intelligence Systems business is a global leader and strategic partner committed to delivering world-class, full life-cycle defense and intelligence products that protect the security of our nation and our allies. From air combat training to state-of-the-art electronic warfare systems, our technology is deployed by virtually all U.S. military and government agencies around the world .

This is a hybrid position open to candidates who reside in or near Beavercreek, Ohio;
Frederick, Maryland; or Fort Walton Beach, Florida.

Own the engine room the whole AI function runs on.

We've used AI before, now we treat it as a first-class citizen at Airborne & Intelligence Systems (AIS). We build and deploy AI across signals intelligence, electronic warfare, air combat training, and the mission systems that connect them. This work shows up on our floor, in the field, and on the company's P&L, while meeting the demanding standards of our defense and commercial customers.

If you want your work and your talent to count, this is the place.

AI has to run on real hardware, and someone has to own it. That's you. You stand up our own AI servers from bare metal - open-weight models, high-performance GPU hosting - keep them running, keep them secure, and fix them when they break. You also build and deploy AI systems on the corporate secure cloud as a developer.

This is a build-and-run role: you don't just stand infrastructure up; you operate it so that what you build keeps working without you in the room. For our own hosting, you are the platform team. On the corporate cloud you are a developer, not the administrator. Corporate IT owns the cloud platform itself (provisioning, networking, identity, patching). You build on it;

you don't run it, and you don't build a competing platform. If that boundary chafes, this isn't the seat; if it reads as clarity, keep going.

At the heart of this function is a proprietary platform, the encoded way this business does AI. It takes any employee from a rough idea to a governed, well-formed use case, checks it against the rules, and prepares a recommendation a human decision-maker rules on. You don't just use it; you also build it. You build and host the platform itself, along with the self-serve tools that carry it to the wider workforce.

The system everyone else uses runs on infrastructure that you own and operate.

This is a high impact, founding individual-contributor seat within the technical core of AI Excellence function with meaningful end-to-end work ownership. You report directly to top leadership of the function giving you outsize visibility, opportunity for personal growth and direct line to impact. Your peers will be AI leads for Solutions & Delivery/Evaluation & Measurement.

Primary & Essential Accountabilities

• Stand up local AI hosting. Model serving, MLOps, and server-level administration for on-premises, self-hosted, and air-gapped environments - setup through sustainment.
• Deploy on the secure cloud. Develop, integrate, and ship AI systems on the corporate GCC High environment as a developer.
• Own security and accreditation engineering. Get our hosting to an authorization-to-operate; partner with the security office; flag capabilities that can't get there.
• Instrument everything you run. Tracing, telemetry, and observability are part of the build, not an afterthought - you'll know it's failing before a user calls.
• Build the tooling everyone uses. Host the shared AI platform tooling and the self-serve tools that reach the wider workforce.
• Ship only what's been cleared. You deploy; a separate teammate runs the quality gate. You're the builder, not the grader of your own work

• 5+ years in infrastructure, platform, or site-reliability engineering (regardless of total years exp), including 3+ years in MLOps or model serving, with recent hands-on self-hosted or LLM-inference/training/tuning work.
• A hands-on record of standing up model-serving or hosting in production and then…