Vetted Python Professionals

“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).”

“Software engineer with 2.5 years at Amazon working on the Package Tracking page (Amazon’s most visited page), delivering new customer actions like Cancel Delivery and Buy Again within a Spring Boot-based stack. Has hands-on experience with feature-flagged rollouts (Weblab), A/B testing, and scaling/load testing for Prime Day-level traffic, and is upskilling into React while leveraging modern AI coding tools on side projects.”

“Backend engineer who built an event-driven, multi-service resume review system integrating AI/ML workflows. Demonstrated strong performance engineering (e.g., composite indexing dropping latency from ~600ms to ~35ms and major P95 gains) and high-throughput pipeline optimization via caching, batching, and worker concurrency tuning, with multi-tenant isolation implemented across DB and Redis.”

“Operator/strategy leader with experience driving digital transformation and building executive operating systems in high-noise environments. At a legacy B2C brand, created a prioritization framework and exec alignment process that cut 100+ initiatives to 16 and lifted execution from 12% to 81%. At Point B, owned a $20M delivery budget while shifting the firm to a tech-forward practice, implementing OKR/KPI cadences and AI-enabled workflows (n8n, LLMs, gamma.ai) that materially improved executive productivity.”

“Full-stack engineer (Snowflake) who shipped an AI/LLM-powered data exploration product end-to-end, spanning Spring Boot/Python services and a polished React UI with streaming responses and robust fallbacks. Experienced operating high-scale AWS deployments (Docker/Kubernetes, SNS/SQS, RDS Postgres, CloudWatch, Jenkins CI/CD) supporting thousands to tens of thousands of concurrent users, including handling real traffic-spike scaling incidents.”

“ML/LLM engineer with production experience building a multi-GPU LLM inference platform using TensorRT and vLLM, achieving ~40% p95 latency reduction through batching/KV caching, quantization, and CUDA/runtime tuning. Also has end-to-end orchestration experience (Kubernetes, Airflow) and has delivered real-time fraud detection systems at Accenture in close collaboration with non-technical risk and product stakeholders.”

“Former multi-startup CTO with experience supporting successful capital raises and working directly with boards/investors. Known for implementing operational systems that reduce client escalations (e.g., a status/semaphore overview board) and for building UX-forward MVPs that evolve into automated platforms, including a loan underwriting system for a small US lender.”

“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.”

“Player Experience developer on Madden who shipped player-facing Weekly Recap and Halftime Show flows built in visual scripting, including a custom solution to keep full-screen UI persistent across level loads with strong logging and failsafes. Also builds networked UE co-op ARPG systems (talent trees, combat/animation, leveling/stats, AI, dynamic spawning) and has a track record of refactoring shared UI/banner logic into scalable prefabs to reduce bugs and improve team workflow.”