Job description

CompassX is a boutique business and technology consulting firm. We help Fortune 500 and high-growth clients deliver their most strategic initiatives, from enterprise transformations to digital and data-driven projects. With over 15 years of proven results, we’ve expanded across industries including financial services, pharmaceuticals, aerospace, consumer products, and quick service restaurants. We are honored to be recognized as a three-time winner of Consulting Magazine’s Best Boutique Firms to Work For, and previously recognized as a “Best Place to Work” in Southern California and one of INC.’s 5000 fastest-growing private companies in the U.S. We're looking for a GenAI Solution Architect who can do both: design the architecture of AI-powered systems and get hands-on building them. This isn't a slide-deck role or a pure advisory role. You'll be orchestrating and building agentic AI systems in real enterprise delivery environments - and helping clients understand what they've actually got. At CompassX, we're delivering AI-enabled solutions for some of the most complex enterprise clients in financial services, pharma, and beyond. We need someone who has moved past the hype and is actively building with the tools - agents, orchestration frameworks, LLM integrations, and the data infrastructure that makes them work in production. This role sits at the leading edge of our AI practice. You'll shape how we deliver GenAI engagements and help define what good looks like for our clients. What You'll Do 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. What You'll Bring 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 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 Our consultants say CompassX is a place where they can truly find their own path. As part of a smaller firm, they see the impact of their work, have an identity, and know their voice matters. They value the freedom to take the reins of their careers, shape their client work, and maintain a direct line to leadership. With CompassX’s focus on serving local Southern California clients, consultants not only build lasting relationships but also see the results of their work in the communities where they live. At CompassX, you won’t just follow a path - you’ll create one.

Requirements

Must-haves

This role sits at the leading edge of our AI practice. You'll shape how we deliver GenAI engagements and help define what good looks like for our clients.
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.
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.
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.
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.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

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Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

Pto

Professional Development