Vetted LlamaIndex Professionals

“GenAI/ML engineer (CitiusTech) who has deployed production RAG systems for compliance/operations document Q&A, using Pinecone + FastAPI microservices on Kubernetes with strong monitoring and guardrails. Also built a GenAI-powered incident triage/routing solution in collaboration with non-technical stakeholders, achieving 35% faster response times and 40% fewer misclassified tickets, and has hands-on orchestration experience with Airflow and AutoSys.”

“Open-source JavaScript contributor focused on performance and maintainability in data visualization libraries—refactored legacy ES5 into modular ES6, added tests/docs, and delivered ~30% faster load times with positive community adoption. Also optimized a React dashboard (~40% load-time reduction) and took ownership in an ambiguous AI product initiative by setting milestones, standing up an initial ML pipeline, and shipping a prototype in ~6 weeks that became the basis for production.”

“ML/GenAI engineer with recent CVS Health experience building a production RAG system over unstructured financial/research documents using LangChain, FAISS, and Pinecone, plus LoRA/PEFT fine-tuning of GPT/LLaMA for domain-aware summarization. Demonstrates strong applied MLOps and data engineering skills (Airflow/Prefect, Docker/Kubernetes, CI/CD, MLflow) and measurable impact (sub-second retrieval, ~40% better context retrieval, ~25% entity matching improvement).”

“GenAI/LLM ML engineer (currently at Webprobo) building an enterprise GenAI platform with document intelligence and automation on AWS and blockchain. Has hands-on experience with RAG, LLM evaluation tooling, and orchestrating production LLM workflows with Apache Airflow, plus deep exposure to reliability challenges in globally distributed/edge deployments. Also partnered with business/marketing stakeholders at a banking client to deliver an AI-driven customer retention insights solution.”

“Built and deployed a production LLM-powered customer support assistant using a RAG backend in Python, focused on deflecting repetitive Tier-1 tickets and reducing resolution time. Demonstrates strong production engineering instincts around reliability (confidence scoring + human fallback), scalability/cost optimization (multi-stage pipelines), and workflow orchestration/observability (LangChain, custom DAGs, structured logging, step metrics).”

“Built and deployed a production-style vision-language pipeline that generates structured medical reports from chest X-rays using BioViLT embeddings, an image-text alignment module, and BiGPT fine-tuned with LoRA, delivered via Streamlit and hosted on AWS EC2. Also collaborating experience presenting EDA findings, feature importance, and model performance to Ford managers while working with vehicle parts data at Bimcon.”

“Master’s-in-Data-Science candidate (UHV) with 4+ years in AI engineering building production LLM and multimodal systems. Designed an LLM-powered workflow automation platform using RAG over vector stores with guardrails (schema/output validation, fallbacks) and a rigorous evaluation/monitoring framework including drift tracking and shadow deployments. Experienced orchestrating large-scale vision-language pipelines with Airflow and Kubernetes (OCR, distributed training) and partnering with non-technical ops stakeholders to cut cycle time and reduce errors.”

“React performance-focused engineer who contributed performance patches back to an open-source context+reducer state helper after profiling and fixing excessive re-renders in an enterprise project management platform at Easley Dunn Productions. Also built an end-to-end LLM-driven pipeline at Prime Healthcare to normalize millions of supply-chain records, reducing defects by 80% and saving 160+ hours/month.”

“AI/LLM engineer who built a production QA RAG for a University of Massachusetts faculty success initiative, cutting service tickets by 70%. Strong end-to-end RAG implementation skills (LangChain, Qdrant, hybrid/HyDE retrieval, FastAPI) with rigorous evaluation (RAGAS, LLM-as-judge) and practical handling of constraints like API rate limits and cost. Prior cross-functional delivery experience collaborating with SMEs and business owners at TCS and IBM.”