Vetted PyTorch Professionals

“Platform/infrastructure-focused engineer with hands-on experience automating AWS EKS upgrades in Python, building observability around logs/metrics/SLOs, and migrating Terraform delivery from CodePipeline to Atlantis. They describe shipping automation that handled 140 resources across 50+ Terraform files ahead of schedule and debugging a Karpenter/CoreDNS regression in Kubernetes by tracing it from regional traffic spikes to a missing local DNS cache configuration.”

“Autonomous driving engineer from iFLYTEK who shipped 5+ middleware modules for vehicles across three models, with deep experience in reliability, IPC performance, and real-world system hardening. Stands out for translating flaky production behavior into measurable signals—resolving 30+ faults, cutting backlog 39%, improving latency 20%, and supporting 500+ hours of road testing with 99%+ reliability.”

“ML/AI engineer with strong end-to-end production ownership across predictive ML and Generative AI use cases. They built a churn prediction platform that cut churn 12% and preserved about $1.2M in annual revenue, and also shipped a RAG-based support assistant that reduced ticket resolution time 30% while improving agent satisfaction and onboarding speed.”

Yash Patel

Screened

Mid AI/ML Engineer specializing in MLOps and cloud platforms

Texas, USA3y exp

“ML/AI engineer with strong end-to-end production ownership across classical ML and GenAI systems. Built and deployed predictive analytics and RAG-based internal tools on AWS/Kubernetes with measurable impact on accuracy, latency, deployment speed, safety, and user productivity.”

Abhirup Chakraborty

Screened

Junior Software Engineer specializing in backend systems, AI, and search

Champaign, IL3y exp

“Built a complex graph-based search engine to find connections between people and has hands-on experience designing multi-agent coding pipelines that move features through implementation, test generation, testing, and sanity checks. Stands out for treating AI agents like an engineering team, with shared-memory coordination, queue signaling, and completeness-focused guardrails to improve reliability and reduce ambiguity.”

“Senior engineering leader with over 20 years of management experience and a hands-on background leading large-scale SaaS, eCommerce, CRM, and customer data platform systems serving millions of users. Stands out for combining deep technical architecture leadership with org-scale people management, including solving multi-tenant SaaS scaling issues, driving self-service product improvements from support patterns, and building governance models for cross-functional delivery.”

“Engineering leader focused on multi-cloud platform modernization, combining deep hands-on expertise in Kubernetes, Terraform, GitOps, and DevSecOps with management of 18-person DevOps/SRE/software teams. Particularly strong in building secure, scalable enterprise SaaS infrastructure and Spark/Databricks data platforms while driving cross-functional standardization, reliability, and faster release cycles.”

“Front-end engineer with experience spanning a real-time warehouse tracking dashboard and internship work on media-heavy mobile web apps embedded in a Swift container. Particularly strong at making complex, fast-changing data understandable in the browser while preserving performance, navigation stability, and user context.”

“Senior engineering leader who scaled a global organization from 15 to roughly 100 people and operates comfortably at both executive and hands-on architecture levels. Has led SaaS platform improvements, AI-based compliance workflow automation with LLM observability, and consumer-facing product modernization using analytics-driven UX decisions.”

Vijayagopalan Raveendran

Screened

Principal Enterprise Architect specializing in AI, cloud, data, and FinTech transformation

Jersey City, NJ20y exp

“Solutions/technical consulting professional with enterprise experience supporting major accounts including Costco, MasterCard, Delta Dental of Michigan, and Fannie Mae. Brings a blend of cloud migration, enterprise architecture, security/IAM integration, and business-case development, plus hands-on automation work in Python and GCP to modernize sales-related data processing.”

“Backend-leaning full-stack engineer with Amazon production experience on book and author search suggestions, plus hands-on work integrating AI agents with Node.js, Bedrock, and OpenAI Assistants. Also built internal admin tools for safety training, OSHA compliance, and digital badge workflows, showing strength in turning ambiguous operational problems into practical shipped products.”

“Shopify full-stack engineer focused on AI/LLM-powered merchant automation products. They have hands-on experience building React/TypeScript and Python/FastAPI systems for long-running agentic workflows, including orchestration, guardrails, observability, and customer-facing trust features, with measurable gains in task completion and latency.”

“Full-stack builder with hands-on experience spanning a MERN e-commerce product, an AI code review agent, and an ambiguous AWS internship project involving PyTorch/TensorFlow support in Redshift. Particularly interesting for recruiters because they combine product-minded engineering with AI agent reliability work, including CI/CD integration, telemetry via Weights & Biases, and measurable impact like 80% reduction in manual review effort.”

Ryan Peterson

Screened

Senior Full-Stack Engineer specializing in Python web and AI applications

New York, USA8y exp

“Backend-heavy full-stack engineer with startup fintech experience who built a real-time lending eligibility service that cut decision time from 12-24 hours to under 5 seconds and lifted conversion by ~15%. Also has B2B SaaS experience automating construction workflows, with strong hands-on work across React/TypeScript, Python/FastAPI, Node.js, Kafka, Redis, and some Go.”

“ML/AI engineer with hands-on ownership of both classical ML and GenAI systems in production. They built an end-to-end churn prediction service on AWS and also shipped RAG-based document search/summarization features, with clear experience in monitoring, hallucination reduction, cost/latency optimization, and creating shared Python/LLM infrastructure used across teams.”

“GenAI/ML engineering leader (player-coach) who built and deployed an image-to-text production system for topology/resource diagrams, combining YOLO-based issue detection with an LLM to generate support-ready reports at scale. Heavy AWS stack (SageMaker, Step Functions, Lambda, CloudWatch, FastAPI, Kubernetes/Docker) with KPI-driven optimization (MTTR, P50), including ~21 custom labels and reported 30–50% faster issue identification while processing thousands of images in production.”