Vetted pandas Professionals

“Backend/data engineer with production experience at Walmart and HealthSnap building Python services and data pipelines on AWS (EKS, Lambda, Glue, Airflow). Strong reliability and operations focus—implemented idempotency + circuit breakers for peak-traffic consistency issues, GitOps CI/CD, and observability. Demonstrated measurable performance wins (Postgres p95 45s to <5s, ~60% CPU reduction) and modernized SAS batch workflows to Python with parallel-run parity validation and feature-flagged rollout.”

“Full-stack, design-minded developer who builds interactive, motion-forward experiences and translates complex creative coding (Three.js/p5.js/GLSL) into accessible UI for non-technical clients. Delivered an end-to-end manufacturing quality control image system for ChargePoint (React dashboard + AWS) and has hands-on field research experience from Hyundai EV user interviews; currently leading development of a virtual gallery for Creative Coding NYC.”

“Built and deployed an agentic, multimodal LLM system that automates privacy redaction pipelines (audio/video/tabular) using LangChain orchestration and a closed-loop self-correction design. Personally implemented and performance-optimized core CV tooling (face blurring with tracking/Kalman filter) achieving >100 FPS on CPU, and validated reliability with golden-dataset benchmarking across 100+ privacy intents and measurable redaction metrics.”

“Machine learning software engineer intern experience at Amazon, where they built a production testing framework to inject frames/videos onto devices to measure embedded CV model inference and ensure broad model compatibility via automatic NNA metadata handling. Also built side projects spanning LLM/RAG orchestration (LangChain/LangGraph with reranking and citations) and applied CV/healthcare work (nail disease detection, medical retrieval chatbot).”

“Built a production RAG-based "knowledge copilot" for support/ops using LangChain/LangGraph, implementing the full pipeline (ingestion, chunking, embeddings, vector DB retrieval/rerank, guarded generation with citations) and operating it as monitored microservices with CI/CD. Also designed an event-driven, streaming backend for real-time inventory ordering predictions that reduced stockouts by 25%, and has hands-on incident response experience stabilizing LLM API latency/5xx spikes using Datadog/APM and resilience patterns.”