15+ years building real systems.

Serve as a Python and AI developer with functional leadership responsibility across AI adoption, platform architecture, document intelligence, machine learning, and AI-assisted software delivery. Played a leading role in ATA's transition toward an AI-native operating model, helping establish the onboarding, policies, technical practices, and workflows needed to integrate AI into daily work across engineering, QA, product, and leadership.

• Helped lead company-wide AI adoption by developing onboarding materials, contributing to responsible-use policies, supporting employees as they integrated AI into their work, and establishing practical patterns for agent-assisted engineering and decision-making.
• Architected and maintained Python, Nix, and container-based services supporting AI, ML, and automation workloads, improving environment reproducibility and reducing deployment and configuration issues.
• Built document-intelligence pipelines using Python, Tesseract, AWS Textract, and structured extraction workflows, eliminating manual processing for selected document classes and achieving more than 90%+ extraction accuracy.
• Designed supervised ML models for form classification and field mapping using Label Studio and Extra Trees, improving automated mapping accuracy by approximately ~40% over the previous approach.
• Owned the architecture and implementation of an AI-assisted QA automation framework that increased automated test coverage to 80% and reduced regression defects by 50%.
• Evaluated custom ML pipelines against managed AWS services to assess scalability, maintainability, implementation effort, and long-term cost; presented build-versus-buy analysis to leadership.
• Helped introduce prose-first, agent-assisted workflows across engineering, QA, product, and leadership, contributing to faster delivery, clearer documentation, and an estimated $250K+ in annual engineering and QA efficiency gains.

Founded and led an AI-driven automation consultancy focused on modernizing business workflows and deploying scalable AI systems.

• Designed and launched AI-powered SaaS platforms, including LLM-based career automation systems with subscription monetization and browser-automation integration.
• Architected secure cloud-native systems and AI integration pipelines.
• Worked directly with clients to translate business problems into deployable AI agent solutions.
• Directed product roadmap, integration strategy, and monetization architecture.
• Explored integration of conversational and multimodal AI systems for customer interaction workflows.