Systems. Teams. Outcomes.

Own architecture and implementation across Python/AI services, document intelligence, ML, automation, and AI-assisted software delivery while helping shape ATA's broader AI operating model. The role combines hands-on system design with cross-functional technical influence across engineering, QA, product, and leadership.

• Defined and socialized practical patterns for AI-assisted engineering and decision-making across the company, including onboarding, responsible-use guidance, workflow design, and hands-on support for teams adopting new tools.
• Architected and maintained Python, Nix, and container-based platform services for AI, ML, and automation workloads, defining reproducible runtime and deployment patterns that reduced configuration drift and operational friction.
• Built document-intelligence pipelines using Python, Tesseract, AWS Textract, and structured extraction workflows, eliminating manual processing for selected document classes and achieving 90%+ extraction accuracy on targeted document classes.
• 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%.
• Owned build-versus-buy evaluation for custom ML pipelines versus managed AWS services, weighing scalability, failure modes, maintainability, implementation effort, and long-term cost and presenting the architectural tradeoffs 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.