Senior Applied AI Architect

CAI•,

2d•Remote

About The Position

CAI is hiring a Sr. Applied AI Architect to serve as the primary technical engine behind our applied AI delivery across all four strategic motions. This is a builder’s role at the intersection of deep technical execution and strategic advisory. We are seeking a highly skilled and experienced Sr. Applied AI Architect to join our IT team. This position will be full-time, remote, and is a salaried position.

Requirements

8-10+ years of experience in applied AI, enterprise software architecture, or AI/ML engineering with at least 3 years in a senior or lead role
Demonstrated track record of designing and delivering production-grade AI solutions end-to-end; hands-on, not advisory
Deep fluency with large language models, agentic AI frameworks, prompt engineering, and MCP or similar orchestration architectures
Hands-on experience with enterprise AI platforms: Anthropic Claude, OpenAI, Databricks, Azure AI / OpenAI Service, AWS Bedrock, or equivalent
Strong system design and integration architecture skills: APIs, data pipelines, event-driven systems, and cloud infrastructure
Ability to operate credibly across technical depth (code environments, architecture review) and executive conversation
Experience working in regulated or government-adjacent environments, with familiarity with FedRAMP, GovRAMP, NIST, or SOC 2 compliance requirements
Strong communication skills: able to translate complex technical architecture into executive-ready artifacts and client-facing narratives
Bachelor's degree in computer science, Engineering, or a related technical field required; master's or advanced degree preferred; or a related field required; or the equivalent combination of education, technical certifications or training, or work experience

Nice To Haves

Experience delivering AI solutions in state and local government, public sector managed services, or workforce services environments
Hands-on experience with Anthropic’s Model Context Protocol (MCP) and multi-agent orchestration frameworks (LangChain, AutoGen, CrewAI, or equivalent)
Track record of building reusable AI architectural assets, patterns, blueprints, frameworks, that scale beyond a single engagement
Experience contributing to AI governance frameworks, responsible AI posture, or client-facing compliance advisory
Certifications in relevant platforms (AWS, Azure, Databricks, Google Cloud AI) or AI governance frameworks (ISACA, NIST AI RMF)
Prior experience in a professional services or consulting delivery model with multiple concurrent client engagements.

Responsibilities

Lead technical design for agentic AI systems, LLM integrations, MCP-based orchestration, and multi-model workflows in internal and external delivery environments
Own build-vs-buy decisions for AI tooling across use cases, working with the CTO organization and design partners
Develop reusable architectural patterns, integration blueprints, and technical playbooks that scale across CAI’s delivery organization
Drive technical POC development from internal initiatives through to client-ready case studies, architecture and implementation
Serve as the technical lead and credibility anchor for AI solution conversations with executives, clients, and procurement stakeholders
Work alongside functional and motion owners to determine whether and how AI applies, then own the technical approach and execution
Pressure-test AI feasibility on CTO assessments, POC scoping, and motion-level AI roadmaps
Mentor and develop junior AI technical staff; contribute to the Applied AI Delivery Team model
Maintain deep, working knowledge of the enterprise AI landscape: LLMs (Anthropic Claude, OpenAI, Gemini), agentic frameworks, MCP architecture, Databricks, vector databases, and emerging tools
Apply hands-on fluency across the full stack CAI deploys; AI/ML frameworks, integration patterns, API design, cloud platforms (AWS, Azure), and security controls relevant to commercial and public sector public sector
Ensure architectural choices align with CAI’s governance standards, NIST AI framework, GovRAMP requirements, and client-specific compliance environments
Bring an evidence-based perspective on AI capability and limitations to keep initiatives grounded, realistic, and defensible.
Package internal AI delivery results into reusable inputs for client-ready case studies, RFP content, and competitive positioning. Build the technical artifacts that move work forward: solution designs, architecture diagrams, integration specs, feasibility assessments, and deployment guides