Fractional AI Architect (Consultant)

Fractional AI Architect (Consultant) Generative AI, ML Systems & Scalable Platform Architecture Contract / Fractional Engagement Remote Overview Bridge-it.ai An AI-driven SaaS platform operating in the career readiness and education technology space is seeking a Fractional AI Architect to conduct an architecture review and provide technical guidance for the platform's AI and data systems. Experience in the U.S. K-12 education ecosystem or EdTech platforms is highly desirable , particularly in systems that support students, educators, counselors, or workforce readiness initiatives. The platform combines Generative AI copilots, retrieval-augmented generation (RAG), knowledge graphs, and traditional machine learning models to support career exploration, pathway planning, and personalized recommendations for students. The engagement focuses on conducting a structured architecture audit and evaluating whether the current system design aligns with the platform's long-term goals for scalability, reliability, observability, and continuous improvement. The consultant will collaborate with engineering and product leadership to identify architectural gaps and provide recommendations for strengthening the AI platform. This role is intended for senior AI architects or principal-level engineers who have previously designed and operated production AI systems at scale. Scope of Engagement The consultant will review the current system architecture and provide recommendations across several key areas. AI Platform Architecture Review Conduct a structured audit of the platform's AI architecture, including: generative AI copilot design agentic workflow orchestration retrieval-augmented generation pipelines knowledge retrieval systems vector database usage knowledge graph integration context management and AI memory strategies prompt and instruction architecture Assess whether the current design supports: reliable AI behavior scalable inference controllable AI workflows maintainable system architecture. Generative AI & LLM Systems Evaluate the architecture and technical strategy related to: LLM model selection API-based vs self-hosted model strategies embeddings and vector search pipelines prompt and context engineering RAG architecture agent orchestration frameworks guardrails and reliability mechanisms. Provide recommendations to improve: model response quality latency cost efficiency system reliability. Traditional Machine Learning Systems Review architecture related to traditional ML use cases such as: recommendation systems predictive analytics forecasting models clustering and segmentation pipelines. Assess the architecture supporting: training pipelines experimentation workflows model deployment model lifecycle management. Copilot Interaction & Agentic Workflows Evaluate the design of AI-driven workflows supporting the copilot experience, including: user-initiated interactions event-driven AI recommendations multi-step reasoning workflows recommendation pipelines. Provide guidance on improving: intent detection workflow orchestration AI reasoning pipelines reliability and safety mechanisms. Platform Architecture & System Design Assess the platform's core architecture, including: microservices architecture event-driven system design message-based communication patterns API architecture service boundaries and modularity. Review the application of architectural patterns such as: event-driven architecture message-driven systems asynchronous processing hexagonal / ports-and-adapters architecture. Provide recommendations for improving: scalability reliability maintainability operational efficiency. Observability, Monitoring & Evaluation Evaluate the platform's ability to monitor both traditional services and AI systems. Assess current capabilities in areas such as: distributed tracing system metrics and logging operational monitoring AI workflow traceability prompt and model evaluation experiment tracking. Provide recommendations for implementing robust observability and evaluation frameworks . Continuous Learning & Feedback Systems Review architecture supporting long-term improvement of AI systems, including: user feedback capture interaction analytics model performance evaluation experimentation frameworks learning pipelines. Provide recommendations for enabling continuous learning and system improvement . Deliverables The consultant will deliver: a structured architecture assessment report identified design gaps and architectural risks prioritized technical recommendations suggested architecture evolution roadmap. The consultant will present findings to the leadership and engineering teams. Required Experience Candidates should have substantial experience designing AI-driven software systems in production environments . Minimum qualifications include: 12+ years of experience building distributed software systems and AI/ML platforms, any less experience - no need to apply strong hands-on experience building Generative AI applications deep understanding of: Retrieval-Augmented Generation (RAG) prompt and context engineering embedding pipelines vector search systems agentic AI architectures practical experience implementing traditional machine learning systems , including: recommendation systems forecasting models predictive analytics pipelines. Software Architecture Experience Demonstrated experience designing modern distributed systems using: microservices architecture event-driven systems message-based system communication asynchronous processing patterns hexagonal architecture / ports-and-adapters. Cloud & Infrastructure Experience building and operating systems on modern cloud platforms such as: Google Cloud AWS Azure. Experience with containerized systems and cloud-native infrastructure. Observability & Production Systems Strong experience operating production systems with: distributed tracing system monitoring and metrics centralized logging operational diagnostics. Experience with AI system observability and evaluation tools is highly desirable. Preferred Experience Experience building AI copilots or conversational AI systems Experience with agent orchestration frameworks Experience with vector databases and knowledge graphs Experience designing AI evaluation pipelines Prior experience in EdTech platforms Familiarity with U.S. K-12 education systems . Engagement Model Fractional consulting engagement (part-time). Initial architecture review phase followed by optional advisory support. Expected duration for the initial engagement: 1–3 months . Ideal Candidate Profile This role is best suited for professionals who have previously served as: Principal Architect AI Platform Architect Staff / Principal Engineer ML Platform Architect AI Infrastructure Architect and who have direct experience building and operating production AI systems.