Senior Applied AI Engineer

Groundswell

1d•$149,748 - $205,305

About The Position

Groundswell is seeking a Senior Applied AI Engineer to join their team. This is a client-facing, hands-on role where you will be responsible for turning ambiguous business problems into working AI solutions. You will work with stakeholders to understand workflows, determine if AI can improve them, design, build, and ensure the AI capabilities are functional in production. The role involves working across the full lifecycle, from discovery to operations, and includes client delivery, internal product development, rapid proofs of concept, and internal enablement. You should be comfortable being the most AI-literate person in diverse rooms and possess a blend of AI understanding, engineering rigor, and communication skills.

Requirements

7+ years building and shipping production software.
At least 2 years of hands-on experience building applications that integrate large language models, including prompt engineering, retrieval-augmented generation, structured extraction, tool use, and agent patterns.
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, with experience integrating APIs and working with imperfect data, 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 with minimal direction in ambiguous situations, surfacing problems worth solving before they are assigned, proposing an approach, and driving it to a decision.
A track record of improving the capability of other engineers, with or without a formal leadership title.
U.S Citizenship required.
Ability to obtain and maintain any federal government background investigation, suitability determination, or security clearance required by assigned client engagements.

Nice To Haves

Master’s degree in a relevant field such as Data Science, Business Analytics, Mathematics, or Computer Science.
Experience with a low-code or application platform such as Appian, OutSystems, Mendix, Microsoft Power Platform, ServiceNow, or Salesforce.
Public sector or regulated-industry delivery experience, including compliance and authorization processes.
Experience with cloud AI services such as AWS Bedrock or Azure OpenAI.
Familiarity with LLM evaluation or observability tooling.
Prior consulting, solutions engineering, professional services, or embedded client work.

Responsibilities

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 client delivery, internal product work, rapid proofs of concept, and internal enablement as the work requires.