Art of Intelligence is a boutique recruiting and business advisory firm built around relationships, trust, and a highly personal approach. We do not advertise or work with a high volume of clients. Instead, we partner with a small, hand-selected group of organizations where we have direct relationships with owners, CEOs, CFOs, and senior leadership. This allows us to truly understand the business, the culture, and the people behind it, and to provide highly targeted recruiting, strategic hiring, business development, and growth support tailored specifically to each client.
Applied AI Engineer- REMOTE / Requires a Public Trust clearance. We sponsor the application; you don't need one already.
We are building AI capabilities for the Department of Veterans Affairs. First up is a developer-assistance chatbot: a retrieval-augmented (RAG) assistant that answers questions about a large financial system by pointing developers to the right internal documentation. Behind it is a pipeline of projects that ranges from hands-on AI work like this to back-end and infrastructure work. You'd be the engineer who builds them and, just as importantly, proves they work.
The AI problems here aren't exotic. The hard part is applying known techniques well: deciding what to build, measuring whether it's helping, and leaving behind something the client can run without us.
Responsibilities
- Design, build, and run AI-backed services on the client's cloud. The chatbot is first; more will follow.
- Own the platform underneath them: infrastructure as code, automated tests, CI/CD, monitoring, and access controls that hold up in a federal environment. We're mostly on Azure and lean on its managed services to keep things simple; any cloud background transfers.
- Set up evaluation from day one: a test set, the right metrics, and a clear go/no-go before we invest further.
- Explain tradeoffs to a non-technical client and make practical calls when the data is incomplete.
Qualifications
- Scientific rigor. You know how to design an experiment, split data properly, and tell signal from noise.
- Solid engineering. You've built and shipped software other people depend on, and you care about how it's built.
- Working AI knowledge. You understand how people actually use chatbots, how to get better answers out of them, and the tradeoffs in retrieval (chunking, embeddings, reranking). Having built a RAG is a plus, not a requirement; so is ML training experience.
- Mid. Roughly 3–6 years post-degree, or a PhD plus some engineering experience.