Vetted Azure Kubernetes Service Professionals

“Entry-level candidate who built NYC Lens, a real-time Gemini-based multi-agent system that processes live camera input, identifies landmarks, and returns structured contextual insights. Despite being a fresher, they show hands-on experience with deployment on Cloud Run, modular orchestration, noisy-data handling, and reliability patterns like retries, fallbacks, and explicit state management.”

Raj Shrinivasan

Screened

Principal AI Engineer specializing in agentic systems and cloud-native platforms

Cleveland, OH23y exp

“Built a production RAG-powered analytics copilot at Aya Healthcare for operations leaders and analysts on a large healthcare staffing platform processing over a billion telemetry records annually. Stands out for strong production-minded agent engineering: deterministic orchestration, grounding-first design, deep observability, and data-driven workflow changes such as confidence-based human review for a PR review agent.”

“Fullstack engineer with strong backend depth who has owned complex digital banking modernization work end-to-end, spanning React UI workflows, Spring Boot microservices, API design, and event-driven integrations. Stands out for balancing technical architecture with user clarity, especially in ambiguous environments where stakeholder feedback reshaped workflows after launch.”

“Full-stack TypeScript engineer with hands-on ownership of a grocery deals/search platform spanning React, Node.js, Supabase, and PostgreSQL. Stands out for designing fuzzy product search, cross-retailer comparison, and price history features, then driving a foundational architectural change to unify product identity with a shared match key that improved reliability and development speed.”

Saketh Kota

Screened

Mid-level Data Scientist / ML Engineer specializing in Generative AI, RAG, and MLOps

Irving, TX4y exp

“Built and productionized a RAG-based LLM research assistant for biomedical and regulatory document search using Mixtral 7B on SageMaker, LangChain, and Milvus, cutting research time by ~40%. Has hands-on multi-cloud MLOps experience across AWS/Azure/GCP with Kubeflow/Airflow/Composer plus Terraform + ArgoCD, and applies rigorous evaluation/monitoring (latency, accuracy, hallucinations). Also partnered with a non-technical PM to deliver an insurance policy Q&A chatbot that reduced customer response time by 30%+.”