Vetted Microsoft Azure Professionals

Richa Chanpuriya

Screened

Mid-level Backend/Agentic AI Engineer specializing in GenAI automation and RAG systems

Remote, India3y exp

“Built and shipped a production AI-driven privacy automation system that autonomously navigates data broker sites to submit opt-out/data deletion requests end-to-end, including robust CAPTCHA detection/solving (e.g., reCAPTCHA/hCaptcha/Cloudflare) via 2Captcha. Experienced in orchestrating stateful LLM agent workflows with LangGraph and hardening them for production with strict state management, retries/fallbacks, validation layers, and database-backed observability/audit logs, collaborating closely with legal/compliance stakeholders.”

“Early-career/learning-stage candidate focused on LLM systems; has not yet built or deployed production AI applications but is actively learning orchestration (Microsoft Semantic Kernel) and core patterns like RAG, embeddings, and model selection based on business requirements.”

“Early-career frontend developer who has built an interactive medieval battle game board in React and JavaScript with real-time state synchronization across a 10x10 grid. Shows strong architectural instincts by separating game logic from UI and has also solved responsive UX issues in a farmer marketplace capstone by redesigning dense desktop-first layouts for mobile readability.”

“AI/LLM engineer who has owned end-to-end production delivery of multi-agent RAG systems on Azure (React + FastAPI + data pipelines + Terraform), including rigorous evaluation/monitoring and reliability guardrails. Shipped an AI-driven observability root-cause analysis assistant that reduced MTTR ~30%, cut alert noise ~20%, and reached ~70% adoption in the first month; also built a clinical document Q&A system with citations and compliance-oriented controls.”

Rutvi Rathod

Screened

Junior Full-Stack Software Engineer specializing in AI and web applications

San Jose, CA1y exp

“LLM/AI backend engineer with hands-on experience taking customer LLM prototypes into production using FastAPI, containerization, CI/CD, and OpenTelemetry-based observability. Demonstrated measurable impact by cutting LLM costs ~40% and reducing workflow errors ~50% through schema-enforced outputs, better tool definitions, retries, and prompt/model optimization; also supports pre-sales via technical discovery and rapid integration demos.”

Kumar Manik

Screened

Intern AI Engineer specializing in LLMs, MLOps, and RAG systems

0y exp

“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”

“Early-career sales candidate with recent nonprofit field sales experience at Key Precision Marketing in Philadelphia, selling causes like ASPCA through in-person outreach at retail locations. Stands out for a relationship-first sales style, quick growth in confidence and objection handling, and a disciplined focus on tracking stops, pitches, closes, and sales.”