Senior Applied AI Engineer — Production AI & Client Impact

Groundswell; United States; Remote; USD 150,000 - 205,000; Full time

Benefits offered by this job

Medical, dental, vision plans

401K match

Paid Time Off

Tuition reimbursement

Flexible schedule

On-site gym and childcare

Job summary

Groundswell is seeking an AI engineer who can turn ambiguous business problems into working AI solutions while knowing when AI is not the answer. This is a client-facing, hands-on role collaborating with stakeholders to understand workflows, design approaches, build, demonstrate, and deploy AI with production accountability across the full lifecycle.

You will operate across client delivery, internal product work, rapid proofs of concept, and enablement, balancing modern AI with engineering rigor

Qualifications

• 7+ years building and shipping production software.
• 2+ years hands-on experience building applications that integrate large language models, including prompt design, 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 Python, TypeScript, or SQL, with experience integrating APIs and working with imperfect data across the full application stack.
• 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 to engineers and executives.
• Excellent written communication producing client-facing docs and rationale.
• Ability to work with minimal direction in ambiguous situations and drive 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/maintain federal clearances.

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.
• 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: prompts, retrieval design, outputs, APIs, data handling, error states, and UI.
• 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.
• Produce clear documentation covering what the solution does, its limitations, validation, and failure handling.
• Monitor quality, cost, latency, and drift after launch, and optimize as better options become available.
• Set direction for clients, shape roadmaps, and support with working proofs.
• Mentor engineers and raise the team’s AI judgment; contribute reusable patterns.

Skills

Production software shipping

LLM integration

Metrics & evaluation

Python/TypeScript/SQL

Requirements gathering

Communication

Autonomy in ambiguity

Mentoring engineers

U.S. Citizenship

Education

Master’s degree in Data Science, Analytics, Math or CS

Tools

AWS Bedrock

Azure OpenAI

Appian

OutSystems

Mendix

Power Platform

ServiceNow

Salesforce

Job description

Groundswell is seeking an AI engineer who can turn ambiguous business problems into working AI solutions while knowing when AI is not the answer. This is a client-facing, hands-on role collaborating with stakeholders to understand workflows, design approaches, build, demonstrate, and deploy AI with production accountability across the full lifecycle.

You will operate across client delivery, internal product work, rapid proofs of concept, and enablement, balancing modern AI with engineering rigor