Vetted PyTorch Professionals

“Engineering leader who drove Lightroom Mobile’s roadmap at Adobe by enforcing reuse of Photoshop imaging tech to deliver RAW, non-destructive mobile editing with full Creative Cloud compatibility. In startup environments, has repeatedly scaled and professionalized engineering orgs (career ladders, evaluation systems, delivery process) and led high-stakes cloud/platform transformations—migrating off Heroku and consolidating fragmented AWS/GCP usage into a single GCP platform—improving reliability, deploy speed, and cutting cloud costs by 20%+.”

“Backend engineer on Amazon’s Geospatial Data team (Amazon Maps) who built a real-time road-layer service ingesting third-party and internal signals to deliver road closures/traffic overlays to delivery drivers on a ~3-minute cadence while minimizing mobile data egress. Demonstrates strong production reliability skills (rate limiting, idempotency, cache stampede prevention) and security depth (IAM, RBAC, tenant row-level security), plus careful handling of edge cases like manual override protection against automated feed overwrites.”

“LLM/agent engineer who built a production code-generation agent at Corvic AI that lets non-technical users query CSV/tabular data in natural language by generating and executing Python. Focused on making LLM systems reliable and scalable via schema-aware validation, sandboxed execution-feedback retries, prompt caching/embeddings, async execution, and high-throughput data processing with Polars; also partnered with Adobe product/marketing to ship brand-aligned AI content generation for email and push notifications.”

“Robotics software engineer with hands-on ROS 2 experience who helped build an autonomous 5-DOF robotic arm that plays Backgammon, owning perception (OpenCV) and game-logic while adding robustness features like lighting tolerance and auto-calibration. Also worked on a Raspberry Pi/LiDAR car project, improving mapping accuracy through data-logged calibration and contributing to multi-robot collision-avoidance coordination via a server-based pub/sub system.”

“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).”

“ML engineer with 5 years at Stripe building and productionizing real-time fraud detection at massive scale (3M+ transactions/day; $5B+ annual payment volume). Delivered measurable impact (22% accuracy lift, 18% loss reduction, +3–5% authorization rates) and has strong MLOps/orchestration experience (Docker, Kubernetes, Airflow, MLflow, CI/CD, monitoring/rollback) plus a structured approach to LLM agent/RAG evaluation.”

“IBM Power/AIX infrastructure engineer who has owned large-scale Power9/AIX 7.x estates across primary/DR (VIOS/HMC/vHMC) and has deep hands-on experience with DLPAR, shared processor pool governance, and PowerHA recovery. Also brings modern DevOps/IaC capability—built Azure DevOps/GitHub CI/CD for Terraform + Kubernetes/Helm with strong security controls and resolved a production Terraform state-lock failure by redesigning backend locking and pipeline concurrency.”