End-to-End AI Solutions Engineer

Groundswell

West Virginia

On-site

USD 88,000 - 172,000

Full time

3 days ago

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Benefits offered by this job

Medical, dental, and vision plans

Flexible Spending Account

4% 401K Match

Paid Time Off

Tuition reimbursement

Professional development

Flexible work schedule

On-site gym

Childcare option

Job summary

Groundswell is seeking an experienced AI engineering leader who can translate business problems into technical AI solutions. You will own capabilities end to end, from discovery to deployment and operation, while guiding teams through architecture decisions and governance.

The role blends client delivery, internal product work, and rapid proofs of concept in a fast-paced federal-facing environment. You will mentor engineers, maintain current AI knowledge, and ensure measurable success criteria

Qualifications

4+ years building and shipping production software.
At least 1 year of hands-on experience building apps that integrate large language models, including prompt engineering, retrieval-augmented generation, structured extraction, tool use, and agent patterns.
Own a capability end to end from ambiguous requirement to production.
Experience evaluating AI system quality with test sets, metrics, and evidence-based decisions.
Strong programming in Python, TypeScript, or SQL across the full stack.
Lead requirements conversations with non-technical stakeholders and translate into a technical approach.
Explain tradeoffs between accuracy, cost, latency, and complexity to engineers and executives.
Excellent written communication for client-facing docs and rationale.
Ability to work independently through ambiguity and drive delivery with minimal direction.
Willingness to teach and develop others.
U.S. Citizenship required.

Responsibilities

Lead requirements conversations with business and technical stakeholders.
Define the technical approach and defend it; select AI patterns appropriately.
Recommend simpler solutions when AI is not needed.
Define measurable success criteria before building.
Build the complete capability end to end, including prompts, retrieval, APIs, data handling, and UI.
Create evaluation sets and iterate based on results.
Make architecture decisions balancing quality, cost, latency, and security.
Operationalize capabilities with governance, logging, and drift monitoring.
Help clients understand possibilities and shape the next phase of work.
Mentor engineers new to AI through code reviews and guidance.
Maintain current models, tooling, and reusable patterns.
Move between client delivery, internal product work, and enablement as needed.
Produce documentation of capability, limitations, validation, and failure handling.