Senior Applied AI Engineer - Client-Facing Production ML

Groundswell; Nevada (IA); On-site; USD 150,000 - 205,000; Full time

Benefits offered by this job

Medical, dental, and vision

Flexible spending account

4% 401K Match

Paid Time Off

Tuition reimbursement

Flexible work schedule

On-site gym and childcare

Job summary

Groundswell is seeking an engineer who can turn ambiguous business problems into working AI solutions and who knows when AI is not the answer. This client-facing, hands-on role collaborates with stakeholders to understand workflows, define success, design the approach, build it, demonstrate value, and own it in production across discovery, build, and operations.

You will operate across client delivery, internal product work, rapid proofs of concept, and internal enablement, moving between roles

Qualifications

• 7+ years building and shipping production software.
• 2+ years hands-on experience integrating large language models.
• Experience evaluating AI system quality, including building test sets, defining metrics, and making deployment decisions based on evidence.
• Strong programming ability in Python, TypeScript, or SQL, with experience API integration and working with imperfect data, across the full application.
• 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 for client-facing documentation and design rationale.
• Ability to work with minimal direction in ambiguous situations, surfacing problems worth solving, 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.

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.

Skills

Production software

LLM integration

APIs integration

Python

Prompt engineering

Non-technical stakeholder comm.

Tradeoff judgment

Leadership mentoring

Security clearance readiness

Education

Master's degree in a relevant field

Tools

Appian

OutSystems

Mendix

Power Platform

ServiceNow

Salesforce

AWS Bedrock

Azure OpenAI

Job description

Groundswell is seeking an engineer who can turn ambiguous business problems into working AI solutions and who knows when AI is not the answer. This client-facing, hands-on role collaborates with stakeholders to understand workflows, define success, design the approach, build it, demonstrate value, and own it in production across discovery, build, and operations.

You will operate across client delivery, internal product work, rapid proofs of concept, and internal enablement, moving between roles