Matthew Lane Freeman

Defined current-state and target-state AI architecture across LLM platforms, data flows, integrations, identity, governance, and human review for 8 enterprise use cases. Led build-versus-buy assessments of 6 AI platforms and vendors, established reference architectures and reusable patterns for routing, RAG, vector search, PII protection, access controls, auditability, and API integration, and guided 5 proofs of concept through operational-readiness gates. Embedded LLMOps practices (CI/CD, telemetry, tracing, drift/performance monitoring, lifecycle controls, and cost dashboards) and partnered with security, compliance, product, and engineering stakeholders to adopt AI roadmaps across multiple teams.

Owned AI architecture for customer-risk and operations workflows, defining LLM, RAG, embedding, agent-harness, and evaluation patterns used across four product teams. Built retrieval and agent prototypes over high-volume transaction, support, and compliance documents to surface key entities, PII signals, timelines, and themes, reducing manual investigation time by 38%. Designed evaluation harnesses for prompt quality, retrieval relevance, safety checks, and regression testing, increasing release confidence across 120+ test scenarios. Implemented model routing and telemetry across Azure OpenAI, Azure AI Foundry, Anthropic, and open-source models, cutting average inference cost by 31% while keeping p95 latency under 2.4 seconds. Led senior engineers through architecture reviews and design documentation, moving six AI services from prototype to production-ready delivery.

Designed secure RAG and semantic-search architecture over structured and unstructured healthcare data, indexing 40M+ records and improving retrieval precision. Built Python AI services including prompt pipelines, vector-search APIs, evaluation harnesses, and human-in-the-loop workflows to reduce latency and manual review effort. Introduced MLOps templates with CI/CD, model registry, experiment tracking, observability, data quality, and drift monitoring, increasing release frequency while reducing rollback time. Collaborated with security/privacy/product/engineering to implement role-based access, encryption, audit trails, lineage, and risk reviews.