Applied AI Engineer — End-to-End AI for Federal Ops

Groundswell; North Carolina; On-site; USD 88,000 - 172,000; Full time

Benefits offered by this job

Medical, dental, and vision plans

Flexible Spending Account

4% 401K Match

Paid Time Off

Tuition reimbursement and professional

development

Flexible work schedule

On-site gym and childcare option

Job summary

Groundswell is seeking an AI-enabled software engineer who will own client-facing capabilities from discovery through deployment. You will lead requirements, craft technical approaches, and decide when AI is appropriate, balancing accuracy, cost, and latency.

You will mentor peers, define metrics, and deliver robust, auditable solutions in complex federal contexts. The role spans client delivery, internal product work, rapid proofs of concept, and enablement.

Qualifications

• 4+ years building and shipping production software.
• At least 1 year hands-on experience building applications that integrate large language models, including prompt engineering, retrieval-augmented generation, structured extraction, tool use, and agent patterns.
• Demonstrated ability to own a capability end to end, from an ambiguous requirement through to something in production that people rely on.
• Demonstrated experience evaluating AI system quality, including building test sets, defining metrics, and making deployment decisions based on evidence.
• Strong programming ability in a general-purpose language such as Python, TypeScript, or SQL, applied across the full application rather than the AI layer alone.
• Proven ability to lead requirements conversations with non-technical stakeholders and translate what you hear into a technical approach.
• Sound judgment on tradeoffs between accuracy, cost, latency, and complexity, with the ability to explain those tradeoffs to both engineers and executives.
• Excellent written communication. This role produces client-facing documentation and design rationale.
• Ability to work independently through ambiguous requirements, define an appropriate technical approach, and drive work through implementation and delivery with minimal direction.
• Willingness to teach. Developing the capability of others is valued alongside individual delivery.
• U.S. Citizenship required. Ability to obtain and maintain any federal government background investigation, suitability determination, or security clearance required by assigned client engagements.

Responsibilities

• Lead requirements conversations with business and technical stakeholders, covering the workflow, the decision being supported, the current standard for acceptable results, and the constraints that were not raised initially.
• Define the technical approach and defend it. 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.
• 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.
• Define measurable success criteria before building, including accuracy targets, human review thresholds, acceptance conditions, and the definition of failure.
• 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. Iterate based on results rather than intuition, and determine when a capability is ready for deployment.
• Make and defend architecture decisions within your scope, weighing quality, cost, latency, security, and authorization constraints.
• Operationalize capabilities in the client environment, including governance, logging, and traceability requirements. Monitor quality, cost, latency, and drift after launch, and optimize as better or less expensive options become available.
• Help clients understand what is possible. Anticipate needs, shape the next phase of work, and build the proof of concept that supports the case.
• Mentor engineers who are new to AI through code review, pairing, and guidance toward the appropriate pattern for a given problem.
• Maintain current knowledge of models, tooling, and techniques, and bring back what proves useful as reusable patterns for the team.
• Move between client delivery, internal product work, rapid proofs of concept, and internal enablement as the work requires.
• Produce clear documentation covering what the solution does, its known limitations, how it was validated, and what happens when it produces an incorrect result.

Skills

Python

TypeScript

SQL

End-to-end ownership

Prompt engineering

Retrieval-augmented generation

Structured extraction

Agent patterns

Communication

Leadership

Mentoring

Decision making

Education

Master's degree in Data Science, Business Analytics, Mathematics, or Computer Science

Tools

AWS Bedrock

Azure OpenAI

Appian/OutSystems/Mendix/Microsoft Power Platform/ServiceNow/Salesforce

Job description

Groundswell is seeking an AI-enabled software engineer who will own client-facing capabilities from discovery through deployment. You will lead requirements, craft technical approaches, and decide when AI is appropriate, balancing accuracy, cost, and latency.

You will mentor peers, define metrics, and deliver robust, auditable solutions in complex federal contexts. The role spans client delivery, internal product work, rapid proofs of concept, and enablement.