Vetted Data Pipelines Professionals

“Backend/platform engineer who has owned real-time reporting and streaming analytics systems end-to-end, combining FastAPI/Postgres APIs with Kafka consumers, Celery background jobs, and Redis caching. Strong DevOps/GitOps experience deploying Python/Node microservices to AWS EKS with Helm, ArgoCD/FluxCD, and CI pipelines, and has supported phased on-prem to AWS migrations using Terraform and traffic cutovers.”

“Senior engineering/technology leader with experience driving board-backed, multi-year platform transformations in retail commerce (American Eagle) and scaling ML/product delivery platforms at Amazon Alexa. Known for data-driven build-vs-buy decisions, org scaling (6→80), and measurable outcomes including 98.5%→99.9% uptime, 234 experiments generating ~$200M revenue lift, and increasing delivery velocity from 1.5 to 30 features/week while transitioning to DNN/large-model architectures.”

Kevin Wang

Screened

Senior Software Engineer specializing in cloud infrastructure and distributed systems

Adams, MA8y exp

“Amazon engineer focused on productionizing LLM-powered developer workflows, including code assistance, debugging automation, and internal AI tooling. Stands out for combining hands-on ML systems work with strong platform engineering, including an orchestration engine that reportedly saved about $10K/day and reduced a manual workflow from 12 hours to under a second.”

Franklin Dickinson

Screened

Senior Data Scientist specializing in product analytics and applied AI

New York, NY9y exp

“Product data scientist at Meta who also built Fogo, a personal finance app, as a solo developer with a React/FastAPI/Postgres stack and sophisticated LLM evaluation infrastructure. Particularly strong in AI backend quality systems, experimentation, and production decision-making around model cost, latency, and reliability.”

“Built and deployed a production LLM conversational AI system at OpenAI supporting chat, summarization, and semantic search at 1M+ requests/day, driving major latency (40%) and accuracy (25%) improvements through Pinecone optimization and tighter RAG with re-ranking. Also has Amazon experience improving recommendation systems by translating ML metrics into business terms to boost CTR and conversions, with strong MLOps/orchestration depth (Airflow, MLflow, SageMaker, Kubeflow).”

Muneendra M

Screened

Mid-level Software Engineer specializing in event-driven backend and AI-enabled systems

San Francisco, CA4y exp

“Full-stack engineer at Stripe who owned a webhook monitoring and retry platform end-to-end, spanning backend services, React dashboards, and production operations. Stands out for combining strong distributed-systems judgment with product polish, including a reported 31% improvement in webhook delivery reliability and UI improvements that reduced support burden.”