Applied AI Engineer

Groundswell

. Define business and technical requirements and the technical approach for AI capabilities .

Posted 9/21/2026full-timeRemote • United StatesMid-LevelSenior💰 $88,177 - $171,637 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in defining and operationalizing AI capabilities, including prompt engineering, retrieval-augmented generation, and structured extraction. Proven ability to lead cross-functional teams, mentor engineers, and communicate effectively with both technical and non-technical stakeholders.

Highest-signal resume keywords

AI Capability DevelopmentLarge Language Model IntegrationPython ProgrammingRequirements GatheringCloud AI Services

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Prompt EngineeringRetrieval-Augmented GenerationStructured ExtractionTool UseAgent PatternsTest Set BuildingMetric DefinitionAPI IntegrationData HandlingError State Management

Soft Skills

Excellent Written CommunicationMentoringIndependent WorkJudgment on TradeoffsStakeholder Engagement

Tools & Technologies

AWS BedrockAzure OpenAIAppianOutSystemsMendixMicrosoft Power PlatformServiceNowSalesforce

Certifications & Qualifications

U.S. CitizenshipFederal Government Background Investigation

Industry Keywords

Public Sector DeliveryRegulated Industry ComplianceAuthorization ProcessesAI System Quality EvaluationDeployment Readiness

Tech Stack

Tools & technologies

AWSAzureCloudPythonServiceNowSQLTypeScript

About the role

Key responsibilities & impact

Define business and technical requirements and the technical approach for AI capabilities
Own capabilities end to end, including discovery, design, development, validation, deployment, and operation
Work across client delivery, internal product development, rapid proofs of concept, and internal enablement
Select appropriate AI patterns such as extraction, classification, summarization, retrieval, or agentic workflows
Recommend simpler non-AI solutions when appropriate
Define measurable success criteria, accuracy targets, human review thresholds, acceptance conditions, and failure definitions
Build complete capabilities including prompt and retrieval design, structured outputs, tool and function definitions, API integration, data handling, error states, and user interfaces
Build evaluation sets from real data, measure results, iterate, and determine deployment readiness
Make architecture decisions considering quality, cost, latency, security, and authorization constraints
Operationalize capabilities with governance, logging, traceability, monitoring, and post-launch optimization
Help clients understand possibilities, anticipate needs, shape future work, and build proofs of concept
Mentor engineers new to AI through code review, pairing, and guidance
Maintain current knowledge of models, tooling, and techniques and develop reusable patterns
Produce documentation covering solution behavior, limitations, validation, and incorrect-result handling

Requirements

What you’ll need

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
Demonstrated ability to own a capability end to end, from an ambiguous requirement through to something in production that people rely on
Demonstrated experience evaluating AI system quality, including building test sets, defining metrics, and making deployment decisions based on evidence
Strong programming ability in a general-purpose language such as Python, TypeScript, or SQL, 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
Ability to work independently through ambiguous requirements, define an appropriate technical approach, and drive work through implementation and delivery with minimal direction
Willingness to teach
U.S. Citizenship required
Ability to obtain and maintain any federal government background investigation, suitability determination, or security clearance required by assigned client engagements
Master’s degree in a relevant field such as Data Science, Business Analytics, Mathematics, or Computer Science (preferred)
Experience with a low-code or application platform such as Appian, OutSystems, Mendix, Microsoft Power Platform, ServiceNow, or Salesforce (preferred)
Public sector or regulated-industry delivery experience, including compliance and authorization processes (preferred)
Experience with cloud AI services such as AWS Bedrock or Azure OpenAI (preferred)
Familiarity with LLM evaluation or observability tooling (preferred)
Prior consulting, solutions engineering, professional services, or embedded client work (preferred)

Benefits

Comp & perks

Comprehensive medical, dental, and vision plans
Flexible Spending Account
4% 401K Match (immediate vesting)
Paid Time Off
Tuition reimbursement, certification programs, and professional development
Flexible work schedule
On-site gym and childcare option