Senior Applied AI Engineer

Who Are We?

Groundswell is a premier technology integrator and solution provider, resolutely committed to solving the most complex challenges facing federal agencies today. Our name, Groundswell, represents our commitment to be an unstoppable, seismic change in government. Ours is a small company culture with big company reach and results. Are you ready to be audacious, be bold and drive change at a rapid pace?

Join us, where we'll make a greater impact together.

We are looking for an engineer who can turn ambiguous business problems into working AI solutions, and who recognizes when AI is not the answer.

This is a client-facing, hands-on role. You will work with stakeholders to understand a workflow, determine whether an AI capability will meaningfully improve it, design the approach, build it, demonstrate that it works, and remain accountable for it in production. You will work across the full lifecycle rather than handing off between discovery, build, and operations.

The work spans client delivery, internal product development, rapid proofs of concept, and internal enablement. You should be comfortable moving between them, and comfortable being the most AI-literate person in a room that includes engineers, executives, and end users.

The role sits at the intersection of three skill sets: understanding what modern AI can and cannot do, the engineering rigor to ship it into production, and the communication skill to run a requirements conversation with someone who has never used the technology.

• Lead discovery with business and technical stakeholders to understand the workflow, the decision being supported, and the current standard for acceptable results.
• Define measurable success criteria before building, including accuracy targets, human review thresholds, acceptance conditions, and the definition of failure.
• Recommend against AI when a simpler solution is the right one. Rules, process changes, and improved interfaces are often the correct answer, and identifying that early is part of the job.
• Select the AI pattern appropriate to the problem, such as extraction, classification, summarization, retrieval, or an agentic workflow, rather than defaulting to the most sophisticated option available.
• Make and defend architecture decisions on where a workload should run, weighing quality, cost, latency, security, and authorization constraints.
• Build the complete capability rather than the AI components alone. This includes prompt and retrieval design, structured outputs, tool and function definitions, API integration, data handling, error states, and the user interface. Adoption usually depends on these supporting elements as much as on model performance.
• Build evaluation sets from real data and measure against them, iterating based on results rather than intuition.
• Determine when a capability is ready for deployment, and identify when it is not.
• Operationalize capabilities in the client environment, including governance, logging, and traceability requirements.
• Produce clear documentation covering what the solution does, its known limitations, how it was validated, and what happens when it produces an incorrect result.
• Monitor quality, cost, latency, and drift after launch, and optimize as better or less expensive options become available.
• Set the direction clients cannot yet articulate. Show them what is possible, shape the roadmap, and support the case with working proof rather than presentation material.
• Raise the technical level of the people around you by mentoring engineers newer to AI, reviewing their work, and building the team's judgment as well as its output.
• Contribute reusable patterns back to the team so that each project does not start from scratch.
• Move between…