Vetted LlamaIndex Professionals

“AI/ML engineer who shipped a production blood-test report understanding and personalized supplement recommendation product, using a LangGraph multi-agent pipeline on AWS serverless with OCR via Bedrock and RAG over vetted clinical research. Also built end-to-end recommender system pipelines at ASANTe using Airflow (ingestion, embeddings/features, training, registry, batch scoring/monitoring) with KPI reporting to Tableau, with a strong focus on safety, evaluation, and measurable reliability.”

“Backend engineer who built an agentic LLM system for private equity/finance that answers questions over enterprise contracts and documents using a vector-db RAG pipeline. Differentiator is a trust-focused citation framework (with highlighted source text) to reduce hallucinations in high-stakes workflows, plus strong DevOps experience deploying microservices on Kubernetes with Helm/GitOps and building Kafka real-time pipelines.”

“Backend engineer who built and evolved a startup data-processing backend (Express.js/MySQL) handling millions of user data points, with a microservices pipeline integrating multiple social media APIs. Emphasizes reliability and security through comprehensive testing, robust error/retry handling for sequential pagination constraints, and tight IAM/JWT/OAuth-based access controls.”

“Data scientist at Guardian Airwaves building a RAG-powered quiz generator using Grok AI, with hands-on experience solving hard document-ingestion problems (PDFs with images/tables) via unstructured.io and LlamaIndex. Has deployed production systems on AWS EC2 and brings a pragmatic approach to agent reliability (human-in-the-loop, LLM-based eval, latency/cost metrics) while effectively translating RAG concepts to non-technical stakeholders.”

“AI Engineer at Humanitarian AI who has built and productionized both a LangGraph-based multi-agent workflow system and a RAG pipeline (OpenAI embeddings + vector DB) with rigorous evaluation/guardrails. Reports strong measurable impact (60% faster workflow delivery, 40% fewer incidents, 70% reduced research time) and has prior enterprise modernization experience at Infosys migrating ETL to microservices with zero production incidents.”

“Built and deployed a scalable, production-ready LLM knowledge assistant using a RAG architecture (LangChain + vector store/FAISS) to replace keyword search for internal documents. Demonstrates hands-on expertise in hallucination reduction and retrieval quality improvements through semantic chunking, similarity tuning, prompt design, and human-in-the-loop validation, plus strong stakeholder communication via demos and visual explanations.”

“AI engineer who shipped a production KYC facial liveness/recognition pipeline (10k+ monthly verifications), including an on-prem, GPU-hosted Qwen3-VL vision-language fallback to detect spoofing/replay attacks. Also helped build a deterministic multi-agent orchestration layer powering a marketplace with Solana on-chain payments, abstracting blockchain complexity behind an API, and has experience translating real-world needs from non-technical stakeholders (construction) into practical document-reading solutions.”

“Senior Data Scientist in consulting who has built production RAG systems for insurance/annuity document search at large scale (100K+ PDF pages), emphasizing grounded answers, guardrails, and low-latency retrieval. Experienced in end-to-end MLOps for LLM apps—monitoring, evaluation sets, drift handling, and safe rollouts—and in orchestrating complex pipelines with Prefect/Airflow and deploying services on Kubernetes.”

“ML/NLP engineer with banking domain experience who built a GenAI-powered fraud detection and risk intelligence system at Origin Bank, combining RAG (LangChain + FAISS), fine-tuned BERT NER, and GPT-4/Sentence-BERT embeddings. Delivered measurable impact (25% higher fraud detection accuracy, 40% less manual review) and emphasizes production-grade pipelines on AWS SageMaker/Airflow with strong data validation and scalable PySpark processing.”

“Built and shipped an agentic RAG chatbot module for NexaCLM to answer questions across large volumes of contracts while minimizing hallucinations and incorrect legal interpretations. Implemented routing between vector retrieval and ReAct-style agent retrieval plus an automated grading/validation layer (cosine-similarity thresholds, retries) and deployed via GitHub Actions to Azure Container Apps, partnering closely with legal stakeholders to define risk/clause-focused objectives.”

“Designed and deployed end-to-end LLM-powered AI agent systems to automate knowledge-intensive workflows across marketing/GTM, recruiting, and support. Brings production reliability rigor (evaluation pipelines, monitoring, testing, A/B experiments) plus orchestration expertise (Airflow, Prefect, custom Python) and a track record of translating non-technical stakeholder goals into working AI solutions (e.g., personalized customer engagement agent at Lara Design).”

“AI/LLM engineer who built a production resume-parsing and candidate-matching platform at Quadrant Technologies, combining agentic LangChain workflows, VLM-based document template extraction (~85% accuracy), and a hybrid RAG backend for resume-to-JD search. Notably integrated automated LLM evals and metric-based CI/CD quality gates to catch silent prompt/model regressions, and led a 3-person team across frontend/backend/testing.”

“LLM/RAG practitioner who has taken a support-ticket triage automation system from prototype to production, building the full pipeline (fine-tuned models, FastAPI inference services, vector storage, monitoring) and delivering measurable impact (~40% reduction in triage time). Demonstrates strong operational troubleshooting of LLM/agentic workflows (observability-driven debugging, fixing agent routing/looping) and supports adoption through tailored demos and sales-aligned technical communication.”

“LLM/RAG engineer who has built and shipped production assistants, including a RAG-based teaching assistant (Marvel AI) using LangChain/LlamaIndex/ChromaDB with OpenAI embeddings and Redis vector search, achieving ~30% accuracy gains and ~35% latency reduction. Also deployed FastAPI services on Google Cloud Run with observability and prompt-level monitoring, and partnered with non-technical ops stakeholders to deliver an internal policy-document RAG assistant.”

“Backend Python engineer who has built an LLM agentic tutoring/assignment helper with a custom pipeline for parsing visually complex textbooks (integrating AlibabaResearch VGT and implementing missing preprocessing from the paper), improving RAG grounding with ~90% cleaner extracted text. Also led major platform scaling work by refactoring monolithic image processing into Celery-based async microservices on AWS (GPU/CUDA + S3), and implemented Kafka streaming for payment webhooks with strict ordering, idempotency, and multi-zone fault tolerance.”

“Developed and productionized VL-Mate, a vision-language, LLM-powered assistant aimed at helping visually impaired users understand their surroundings and query internal knowledge. Emphasizes reliability and safety via confidence thresholds, uncertainty-aware fallbacks, hallucination grounding checks, and rigorous offline + user-in-the-loop evaluation, with experience orchestrating multi-step LLM pipelines (LangChain-style and custom Python async) and deploying on containerized infrastructure.”

“Backend engineer with legal-tech and AI workflow experience: built JurisAI, an end-to-end legal research system using OCR + embeddings + Pinecone vector search to deliver citation-grounded LLM answers with safe failure modes (~90% recall@K). Also led a GW Law metadata migration into Caspio with batch validation and parallel rollout, and has strong FastAPI/GCP production reliability and observability practices.”

“Built and deployed a production GenAI resume screening and matching system for Florida Atlantic University, focused on improving recruiter efficiency and search relevance. Demonstrates strong RAG engineering (embeddings, query rewriting, metadata filtering, threshold tuning) plus practical reliability work (grounding constraints, fallbacks, and evaluation using real user queries) using Python REST APIs and orchestration frameworks like LangChain and LlamaIndex.”