Vetted MLOps Professionals

Vijayagopalan Raveendran

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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.”

“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.”

Santhoshkumar Biradar

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Principal Product Leader specializing in AI search and intelligent enterprise commerce

Union City, CA25y exp

“Principal Product Manager with 8+ years leading a large-scale AI search platform for SAP e-commerce products, owning strategy through execution for a system serving 15+ billion documents and 50 million queries per day at 99.99% uptime. Built the product from 0 to 1 using RAG, vector search, embeddings, and LLMs, then scaled it iteratively across 9 applications and multiple global languages while navigating complex cross-functional and customer-driven priorities.”

“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.”