Applied AI Engineer: End-to-End AI Delivery Leader

Groundswell; Virginia (MN); On-site; USD 88,000 - 172,000; Full time

Benefits offered by this job

Medical plans

Dental plans

Vision plans

4% 401K Match

Paid time off

Tuition reimbursement

Job summary

Groundswell is seeking an Applied AI Engineer to move business needs from early requirements through design, building, evaluation, and operationalization of AI capabilities for client work and internal use. The role blends practical software engineering with AI-literate leadership and the ability to define success with measurable criteria.

The position requires 4+ years shipping production software, hands-on AI integrations, and the ability to own a capability end to end. U.S.

Qualifications

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

Responsibilities

• Lead requirements discussions with business and technical stakeholders, including the workflow, the decision the system will support, current expectations for acceptable results, and constraints that were not surfaced initially.
• Define and defend the technical approach.
• Select an appropriate AI pattern for the problem, such as extraction, classification, summarization, retrieval, or an agentic workflow, instead of defaulting to the most advanced option.
• Recommend against AI when a simpler solution better fits the needs.
• Identify when rules, process changes, or improved interfaces are the correct path, early in discovery and delivery.
• Define measurable success criteria before building, including accuracy targets, human review thresholds, acceptance conditions, and the definition of failure.
• Build the full capability, not only AI components, including prompt and retrieval design, structured outputs, tool and function definitions, API integration, data handling, error states, and the user interface.
• Base readiness decisions based on evidence from evaluation and iteration rather than intuition.
• Build evaluation sets from real data and evaluate against them.
• Make and defend architecture decisions within scope, weighing quality, cost, latency, security, and authorization constraints.
• Operationalize capabilities in the client environment, including governance, logging, and traceability requirements.
• After launch, monitor quality, cost, latency, and drift, and optimize as improved or lower-cost options become available.
• Help clients understand what is possible and shape subsequent phases of work, including proof of concept development.
• Mentor engineers new to AI through code review, pairing, and guidance on selecting an appropriate pattern for a given problem.
• Maintain current knowledge of models, tooling, and techniques, and contribute reusable patterns to the team when they prove effective.
• Move among client delivery, internal product work, rapid proofs of concept, and internal enablement as needed.
• Create clear documentation describing what the solution does, known limitations, validation approach, and what to do when the system produces incorrect results.

Skills

Requirements gathering

Stakeholder communication

End-to-end ownership

Python

TypeScript

SQL

MLE integration

Decision making

Communication

Evaluation metrics

Education

Master’s degree in Data Science or related field

Tools

Appian

OutSystems

Mendix

Microsoft Power Platform

ServiceNow

Salesforce

AWS Bedrock

Azure OpenAI

Python

TypeScript

SQL

Job description

Groundswell is seeking an Applied AI Engineer to move business needs from early requirements through design, building, evaluation, and operationalization of AI capabilities for client work and internal use. The role blends practical software engineering with AI-literate leadership and the ability to define success with measurable criteria.

The position requires 4+ years shipping production software, hands-on AI integrations, and the ability to own a capability end to end. U.S.