Solution Architect – GenAI

CompassX Group

GenAI Solution Architect designing the architecture and building AI-powered systems for Fortune 500 clients. Delivering AI-enabled solutions in complex enterprise environments and assisting clients with implementation.

Posted 5/26/2026full-timeIrvine • California • 🇺🇸 United StatesMid-LevelSenior💰 $150,000 - $190,000 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoGoogle Cloud PlatformPython

About the role

Key responsibilities & impact

Architect GenAI Solutions: Design end-to-end architectures for AI-powered applications - from LLM selection and prompt engineering through orchestration, retrieval-augmented generation (RAG), and integration with enterprise data environments.
Build and Orchestrate Agents: Go beyond design - build agentic AI workflows using frameworks like LangChain, LangGraph, AutoGen, CrewAI, or similar. You know how to orchestrate multi-agent systems and have shipped something real.
Own the AI Stack: Work across the full GenAI delivery stack: LLM APIs (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock), vector databases, RAG pipelines, evaluation frameworks, and deployment infrastructure.
Bridge to Enterprise Systems: Integrate GenAI solutions with existing enterprise architecture - data platforms, APIs, identity and access controls, and compliance requirements. Make AI work within the constraints of real organizations.
Advise Client Leadership: Translate technical architecture decisions into clear strategic narratives for client leadership. Help clients move from AI experimentation to AI-enabled operations.
Accelerate the Team: Bring patterns, reusable components, and technical standards to the delivery team. Be the person who makes everyone else faster.

Requirements

What you’ll need

Hands-On GenAI Experience: You have built production or near-production GenAI systems - not just demos. You've wrestled with evaluation, hallucination, latency, and cost at the architecture level.
Agentic AI Fluency: Practical experience building agentic workflows and multi-agent systems using frameworks like LangChain, LangGraph, AutoGen, CrewAI, or similar. You understand where these tools shine and where they fall apart.
LLM Platform Breadth: Hands-on experience with major LLM platforms and APIs: OpenAI, Anthropic Claude, Azure OpenAI, AWS Bedrock, or equivalent. You know the trade-offs between them.
Engineering Foundation: Strong Python skills and comfort in cloud-native environments (AWS, Azure, or GCP). You can write the code, not just spec it.
Data Architecture Context: Understanding of how AI systems consume data - vector stores, embeddings, RAG architectures, and the data infrastructure that sits underneath production AI applications.
Consulting & Communication: Experience working in client-facing or consulting delivery contexts. You can navigate organizational complexity and explain AI architecture to both technical teams and senior business stakeholders.
Builder Mentality: You default to building. You use AI tools to accelerate your own work. You're excited to operate at the frontier of what's actually deployable in enterprise environments - not just what's theoretically possible.

Benefits

Comp & perks

Competitive salary with performance-based bonus
PTO, holidays, and sabbatical program
Health, dental, vision, and life insurance
Retirement plan with company match from day one
Learning and professional development support
Small-firm culture with direct access to leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

GenAI architectureprompt engineeringmulti-agent systemsLLM APIsPythondata architecturevector databasesRAG pipelinesevaluation frameworkscloud-native environments

Soft Skills

consultingcommunicationorganizational navigationstrategic narrative developmentteam accelerationbuilder mentality