Senior AI Engineer — Production AI for Client Delivery

Groundswell; Minnesota; On-site; USD 150,000 - 205,000; Full time

Benefits offered by this job

Comprehensive benefits

On-site gym

Tuition reimbursement

Job summary

Groundswell is seeking an experienced engineer to turn ambiguous business problems into working AI solutions. This client-facing, hands-on role requires designing, building, and operationalizing AI capabilities across the full lifecycle with strong collaboration across stakeholders.

You will define success criteria, select AI patterns, and ensure architecture and governance are in place. The role emphasizes production readiness, measurable outcomes, and clear documentation.

Qualifications

• 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.
• 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.

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.
• Select the AI pattern appropriate to the problem, such as extraction, classification, summarization, retrieval, or an agentic workflow.
• 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.
• Build evaluation sets from real data and measure against them, iterating based on results.
• 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.
• 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

Python

TypeScript

SQL

APIs

Prompt engineering

Retrieval-Augmented Generation

Education

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

Tools

Appian

OutSystems

Mendix

Power Platform

ServiceNow

Salesforce

Job description

Groundswell is seeking an experienced engineer to turn ambiguous business problems into working AI solutions. This client-facing, hands-on role requires designing, building, and operationalizing AI capabilities across the full lifecycle with strong collaboration across stakeholders.

You will define success criteria, select AI patterns, and ensure architecture and governance are in place. The role emphasizes production readiness, measurable outcomes, and clear documentation.