This portfolio doesn't just show code—it demonstrates the core competencies required for senior technical program management in AI-enabled environments.
Building this system required the same skills needed for enterprise-scale delivery: architecture decisions, vendor orchestration, quality control, and shipping under constraints—just with AI as a force multiplier instead of a 20-person team.
Designed 3-tier pipeline from scratch (ingestion → scoring → visualization) with repeatable runs, clean data provenance, and extensibility without foundation rewrites.
Chose tools strategically (PostgreSQL vs SQLite, GPU optimization, Groq vs local LLMs), validated tradeoffs, managed constraints without overbuilding.
Managed multiple AI "consultants" (Claude for implementation, Grok for creative thinking, ChatGPT for validation) while maintaining decision authority and architectural ownership.
Caught AI errors (wrong statistical tests, bad schema designs, broken HTML), enforced statistical rigor (baselines, t-tests, effect sizes), maintained engineering standards throughout.
Shipped complete end-to-end system in 6 weeks while working full-time at Google on DMA-15 compliance—timeboxed scope, managed MVP boundaries, hit deadline.
Documented failures alongside successes (AI hallucinations, context window limits, broken iterations), maintained honest scope boundaries, created auditable decision trail.
The same judgment that caught bad AI suggestions catches bad vendor proposals. The same discipline that managed 9 BERT models manages 20-team coordination. The composure that debugged inference pipelines at 2am handles regulatory fire drills.
Defined MVP boundaries explicitly (9 dimensions, HN corpus only, no real-time predictions), resisted feature creep, shipped what could be validated.
Built guardrails against "confidently wrong" AI (structured outputs, verification loops, statistical baselines), documented failure modes, maintained audit trail.
Preserved failed iterations in /deprecated folders (learning artifacts), rebuilt cleanly when patches accumulated, maintained architectural integrity through 4+ major refactors.
Created architecture docs, technical specifications, how-to guides—22 tracked tasks from concept to deployment, maintaining documentation as first-class deliverable.
I didn't build this to prove I can code. I built this to prove I can architect, validate, and ship—using AI as a force multiplier the same way I'd orchestrate vendors, consultants, or cross-functional teams. That's the skill that matters for senior technical roles in 2025 and beyond.