AI Solutions Engineer

Brillio

. Engage with business stakeholders, VPs, clinical leads, and operations owners to identify high-value problems and translate them into AI opportunities .

Posted 10/4/2026full-timeRemote • New York • United StatesSeniorLead💰 $150,000 - $250,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in healthcare operations and AI engineering, with a focus on developing full-stack AI applications and prototypes. Proficient in translating business needs into AI solutions while ensuring compliance with healthcare regulations and responsible AI practices.

Highest-signal resume keywords

Healthcare Industry ExperienceClaude API DevelopmentFull-Stack AI EngineeringAI Governance KnowledgeROI and Impact Measurement

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI PrototypingLLM IntegrationRAGREST API DevelopmentPrompt EngineeringData Pipeline ArchitectureBias EvaluationModel MonitoringAudit LoggingEnterprise AI Governance

Soft Skills

Stakeholder EngagementTechnical CommunicationProblem StructuringRelationship BuildingFeedback Iteration

Tools & Technologies

ClaudeLangChainLangGraphCrewAIAutoGen

Industry Keywords

Health Insurance OperationsClinical WorkflowsHIPAA ComplianceFHIRICD-10CPTClaims Data StructuresCMS RegulationsFDA Digital Health GuidanceAI Accelerator Library

About the role

Key responsibilities & impact

Engage with business stakeholders, VPs, clinical leads, and operations owners to identify high-value problems and translate them into AI opportunities
Define minimum viable AI solutions, map processes, identify opportunities to reduce friction and cost, and accelerate decisions
Create scoping documents, ROI estimates, and success criteria
Design and build AI prototypes within days using Claude and modern AI development tools
Develop full-stack AI applications involving LLM orchestration, RAG, agentic workflows, REST API integrations, and lightweight front-end interfaces
Own technical architecture from prompt design through data pipeline and user interface
Iterate using real user feedback and drive prototypes toward production-grade quality
Use Claude for code generation, testing, documentation, and solution design
Partner with the AI Governance team to validate and harden prototypes for enterprise deployment
Apply responsible AI practices including bias review, explainability, HIPAA compliance, audit logging, and model monitoring
Document solutions for handoff, scaling, and maintenance
Contribute reusable patterns to Brillio's AI accelerator library
Participate in sprint reviews, program steering committees, executive updates, live demos, and client business rhythm meetings
Communicate technical concepts to non-technical leaders and surface blockers, dependencies, and risks
Develop deep expertise in client workflows, data, competitive pressures, and regulatory environments
Build trusted-advisor relationships with healthcare business counterparts
Share client engagement learnings with Brillio's applied AI community

Requirements

What you’ll need

8+ years of healthcare industry experience, specifically direct hands-on experience in health insurance operations or health system clinical and operational workflows
Demonstrated use of the Claude API as a development tool, including prompt engineering, tool use, and agentic patterns
Full-stack AI engineering capability, including LLM integration, RAG, agentic orchestration, REST API development, and lightweight front-end interfaces
Proven ability to move from a business conversation to a working AI prototype within days
Ability to explain technical solutions, costs, and next steps clearly to healthcare executives without jargon
Comfort participating in steering committees, executive briefings, and business rhythm meetings
Ability to structure ambiguous business problems into scoped AI opportunities with clear inputs, outputs, and success criteria
Ability to build credible ROI and impact measurement business cases
Strong preference: hands-on experience with LangChain, LangGraph, CrewAI, AutoGen, or equivalent frameworks
Strong preference: familiarity with HL7, FHIR, ICD-10, CPT, claims data structures, HIPAA, CMS regulations, and FDA digital health guidance
Strong preference: forward-deployed, embedded, consulting, or startup engineering experience
Strong preference: experience shipping AI in a health insurance technology company or health system
Strong preference: experience contributing to reusable AI platforms or accelerators
Strong preference: knowledge of AI governance, responsible AI, model risk management, bias evaluation, audit logging, and enterprise AI governance frameworks