Associate Applied AI Engineer

Groundswell•Washington, NV

9d•$88,177 - $171,637

About The Position

Groundswell is seeking an engineer who can translate technical approaches and incompletely defined business needs into working software. This role involves client-facing responsibilities, working directly with business stakeholders to refine requirements and build solutions. The engineer will be expected to propose better approaches and will take on responsibility for requirements definition and technical design as they develop. The work encompasses client delivery, internal product development, rapid proofs of concept, and internal enablement, requiring a broad range of skills rather than specialization. A key aspect of the role is helping customers understand the potential of AI in their work, as many do not yet have a clear picture. This is a hands-on role where you will work directly with business stakeholders to refine the requirements around a business need and build the result. Proposing a better approach when you see one is expected. Requirements definition becomes your responsibility early, and technical design follows as you develop. The work spans client delivery, internal product development, rapid proofs of concept, and internal enablement. Some weeks are spent primarily building functionality and others in front of customers. We are looking for range rather than specialization. Most customers do not yet have a clear picture of what AI can do for their work. Helping them develop one is part of this role.

Requirements

Bachelor’s degree in a technical field, or equivalent demonstrated ability.
1+ years of professional software development experience.
Hands-on experience building applications that integrate large language models, including prompt engineering, retrieval-augmented generation, structured extraction, and/or tool use. Experience may be gained through professional work, internships, academic research, or significant personal/open-source projects.
Experience contributing to applications that integrate AI capabilities with APIs, data handling, and user-facing functionality.
Familiarity with a general-purpose language such as Python, TypeScript, or SQL, with experience integrating APIs and working with imperfect data.
Ability to conduct a requirements conversation with a non-technical stakeholder, identify the underlying need, and return with a proposed approach.
Willingness to question a design, including one you have been given, and to make the case for an alternative.
Ability to work through ambiguous requirements with guidance, ask thoughtful questions, and drive assigned work to completion.
Clear written and verbal communication. This role produces documentation and materials that clients read.
Candor about the limits of your knowledge and a willingness to ask rather than assume.
U.S. Citizenship required.
Ability to obtain and maintain any federal government background investigation, suitability determination, or security clearance required by assigned client engagements.

Nice To Haves

Prior client-facing work in consulting, solutions engineering, professional services, or implementation.
Experience with a low-code or application platform such as Appian, OutSystems, Mendix, Microsoft Power Platform, ServiceNow, or Salesforce.
Public sector or regulated-industry delivery experience.
Experience with cloud AI services such as AWS Bedrock or Azure OpenAI.
Familiarity with LLM evaluation or observability tooling.
Personal or open source projects involving language models, agents, retrieval, or evaluation.

Responsibilities

Take a defined technical approach and build it into production-quality functionality.
Participate in requirements discussions with business stakeholders to understand the workflow, business need, and desired outcome.
Contribute ideas and alternative approaches during solution design, including identifying opportunities to simplify the solution or reduce the AI component where appropriate.
Contribute to building complete applications that integrate AI capabilities with prompt and retrieval design, structured outputs, tool and function definitions, API integration, data handling, error handling, and user-facing functionality.
Build evaluation sets from real data, measure results against them, and report findings accurately, including results that do not support the chosen approach.
Investigate failures by identifying where a capability produced an incorrect result, characterizing the cause, and implementing a fix.
Support client demonstrations and proof-of-concepts to help customers understand how AI can be applied to their business challenges.
Seek feedback, ask thoughtful questions, and continuously build your understanding of client needs and AI best practices.
Instrument and document your work, including logging, traceability, intended behavior, known limitations, validation method, and failure handling.
Move between client delivery, internal product work, rapid proofs of concept, and internal enablement as the work requires.
Raise blockers, unclear requirements, and results you do not trust early rather than working around them.
Contribute reusable patterns, prompts, and evaluation harnesses back to the team.