• Designed and shipped a feature-flagged, slot-in replacement generation service (Azure OpenAI, JSON

Schema, TypeScript/Python) delivering 98.5% schema-compliant outputs across 8 data types; reduced

schema-breaking failures by 90.6%.

• Built JSON normalization and automated 1,000+ generation scale tests; reduced retries 78.5% and API calls 29% (lower cost, faster UX).

• Improved worst-case hierarchical type success ~49% → ~99% via targeted curation/tuning; released with zero downtime and per-type logging.

• Piloting rubric-based content-quality evaluation aligned to standards to reduce type-incongruent outputs

• Delivered 1:1 consulting for engineers on systems design, data engineering, and AI/ML stack integration (requirements → tradeoffs → architecture → implementation plan).

• Guided backend/API design, ETL/ELT patterns, scalability/reliability practices, and practical LLM

integration (evaluation, guardrails, deployment).

• Supported modernization across cloud-first architectures, CI/CD workflows, and performance/cost tuning.

• Delivered backend for live-streaming; scaled to 1,000+ concurrent listeners with real-time pipelines,

dashboards, and monitoring.

• Implemented GitHub Actions CI/CD for automated builds/rollouts, enabling safe and frequent releases.

• Migrated legacy monoliths into containerized microservices; improved scalability and deployment

reliability.

• Mentored a team of 5 developers through code reviews, planning, and delivery support.

• Developed an automated migration pipeline to migrate SQL Server data to AWS Neptune Graph DB using Pandas.

• Redesigned data model on Neptune to support O(1) lookups on critical paths, eliminating linear scans and reducing storage 15.8×

• Deployed API endpoints using Flask; tool repurposed by R&D for Snowflake and other DB migrations.