Vetted Retrieval-Augmented Generation (RAG) Professionals

“Frontend-focused JavaScript engineer who built Collabsync with real-time chat and file sharing using Socket.io, emphasizing reusable components, clear event contracts, and performance (minimizing React re-renders). Has experience at PwC building internal React/Angular dashboards and documenting insurance APIs, plus a contract role at Capital One delivering in fast-changing, loosely defined environments.”

“20-year technology builder with ~8 years in healthcare AI, currently at Keycentrix modernizing a legacy pharmacy solutions business. Shipped an OCR MVP within days and delivered a rebate-based product generating ~$50K/month, leveraging Claude/LangGraph agentic automation to replace work typically requiring a much larger engineering team. Developing a "Longevity AI Copilot" B2B platform that synthesizes research, labs, and wearable data into personalized longevity protocols for HNW and corporate wellness markets; concept validated but not yet incorporated or funded.”

Pratik Jaiswal

Screened

Mid-level AI/ML Engineer specializing in financial services ML and MLOps

Remote, USA4y exp

“ML engineer/data scientist with M&T Bank experience who built a production reinforcement-learning portfolio analytics tool for wealth management, emphasizing near real-time performance via batch/serving separation and robust generalization through stress-scenario backtesting and RL regularization. Strong MLOps background (Airflow, Grafana, MLflow) and proven ability to drive adoption with non-technical stakeholders using KPI alignment and SHAP-based explanations.”

“Backend/AI engineer who has built an offline, citation-grounded RAG system end-to-end with hybrid retrieval, local LLM inference, and quantitative evaluation via RAGAS. Also brings real-time systems experience from an Airbnb-like booking platform and data pipeline/ML quality work from a Bilibili internship, with a strong emphasis on reliability, privacy, and measurable correctness.”