Dan Raymond

About Dan Raymond

AI transformation leader with 15+ years of engineering and enterprise modernization experience, driving large-scale automation, DevSecOps evolution, and AI adoption across regulated and mission-critical environments.I operate at the intersection of AI strategy, platform engineering, and organizational enablement—translating emerging AI capabilities into measurable business outcomes, executive alignment, and scalable operational models.Core leadership impact:• Spearheaded enterprise transition to a prose-first AI agent operating model, embedding AI agents across engineering, QA, product, and executive workflows—improving cross-functional productivity by 30% and eliminating up to 100% of select manual processes• Delivered multimillion-dollar efficiency gains across defense and commercial programs through automation, DevSecOps modernization, and AI-enabled workflow redesign• Defined AI platform architecture and governance standards, including build-vs-buy evaluations (custom ML vs. AWS Comprehend) to guide long-term enterprise AI strategy• Built and scaled secure, reproducible AI and automation platforms using Python, Nix, Kubernetes, and cloud-native infrastructure• Led cross-functional initiatives influencing 100+ engineers, cyber teams, product stakeholders, and executive leadershipMy leadership approach combines technical depth with executive clarity—aligning AI innovation with cost efficiency, delivery velocity, security posture, and long-term organizational capability.

Experience

1. Senior Ai Engineer (Platform & Ai Lead)

   Kforce Inc

   Nov 2025 — Present · US

   Led development and optimization of AI- and ML-driven automation and document intelligence systems, reducing manual workload by up to 100%(saving an estimated $250k+ annually in engineering and QA efficiency gains). Spearheaded the company’s transition to a prose-first AI agent model, enabling engineering, QA, product, and executive teams to integrate AI agents into daily workflows, improving cross-team productivity by 30%. Established architectural standards in Python and Nix for reproducible AI platforms and secure deployment pipelines.• Architected and maintained containerized microservices and LLM-powered pipelines using Python and Nix, reducing environment inconsistencies by 70% and cutting troubleshooting time by 100+ hours per quarter.• Drove adoption of AI agents across development, QA, product management, and leadership functions, accelerating feature delivery timelines by 25% and improving documentation clarity and decision velocity.• Built end-to-end OCR and document intelligence pipelines (Tesseract, AWS Textract, Python), decreasing manual processing time by up to 100% and improving structured data extraction accuracy to 90%+.• Designed and trained custom ML models for form processing and field classification using Label Studio and Extra Trees, increasing automated field-mapping accuracy by 40% and reducing reliance on manual rule-based systems.• Evaluated and prototyped migration of in-house ML pipelines to AWS Comprehend to assess scalability, maintainability, and cost efficiency, providing data-driven recommendations for long-term AI strategy.• Single-handedly designed and implemented the project’s AI-driven QA automation framework, increasing automated test coverage to 80% and reducing regression defects by 50% while embedding intelligent agents into testing workflows.

Education

• University of Colorado Colorado Springs

  Bachelor of Innovation in Business, Information Systems with Engineering Cross-Core

  2011 — 2016