Vetted Machine Learning Professionals

“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.”

Utkarsh Mittal

Screened

Mid-level Software Engineer specializing in backend and distributed systems

Murray Hill, NJ5y exp

“Backend engineer who has owned large-scale systems from design through rollout, including a Dell rearchitecture that unified two regional platforms and delivered major latency gains while scaling the effort from 6 to 150 contributors. Also built an ESG analysis product in an ambiguous startup environment using AWS Lambda, Bedrock/Claude, Flask, and custom data pipelines, showing a blend of distributed systems depth and practical AI integration.”

“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.”

“Product leader with experience rebuilding large-scale search and AI-enabled workflow products at Amadeus and SignalWire. Stands out for combining AI strategy, UX simplification, and organizational alignment—driving measurable outcomes like 45% adoption growth, 42% revenue increase, and mentoring teams to stronger cross-functional execution.”

“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.”

Shane Westra

Screened

Executive product and technology leader specializing in B2B SaaS

27y exp

“Senior product leader with a track record of building and repositioning B2B software businesses into major growth engines, including initiatives that contributed to Workfront’s $1.5B Adobe acquisition and SimpleNexus’s $1.2B nCino acquisition. Brings deep experience across product strategy, UX transformation, AI-enabled workflows, and cross-functional org leadership, with repeated success turning research insights into high-impact products.”

“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.”

“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.”