Vetted Vector Databases Professionals

“Asset Management Risk professional at Fidelity Investments who built and productionized an agentic RAG platform enabling compliance and analysts to query 10,000+ fund documents with cited answers in seconds. Implemented structure-aware semantic chunking (AWS Textract), hierarchical retrieval, and hybrid search to raise accuracy from 68% to 94%, and built an evaluation framework tracking accuracy/latency/cost/hallucinations—delivering 40+ hours/month saved and zero critical production failures.”

“ML/GenAI practitioner with healthcare domain depth who built and deployed a production cervical-cancer EMR classification system using a hybrid rules + medical BERT approach, optimized for high recall under severe class imbalance and PHI constraints. Experienced running end-to-end production ML/LLM pipelines with Apache Airflow (validation, promotion/rollback, monitoring, retraining) and partnering closely with clinicians to calibrate thresholds and implement human-in-the-loop review.”

“LLM/agent engineer who has shipped production RAG chatbots in sustainability-focused domains, including a packaging recommendation assistant that standardized messy user inputs and used Pinecone-backed retrieval over product/regulatory data. Experienced orchestrating end-to-end ML workflows with Airflow and AWS Step Functions/Lambda, emphasizing reliability (property-based testing, circuit breakers, OpenTelemetry) and measurable performance (latency/cost). Partnered closely with non-technical leadership to ship 3 weeks early, driving adoption by 150+ businesses and ~20% reported waste reduction.”

“Amazon Fire TV Devices engineer who built and shipped a production LLM-powered lab triage and validation system that grounds recommendations in internal runbooks/known-issue data and pushes evidence-based actions via dashboards and Slack. Emphasizes safety and measurability with structured JSON outputs, replay-based evaluation on historical incidents, and production metrics (e.g., disagreement rate and time-to-first-action), plus cost/latency optimizations like caching, batching, and rule-based fast paths.”

“Software engineer with experience at Google and the City and County of San Francisco building production AI systems, including a RAG-based internal support chatbot and ML-driven ticket priority tagging. Has scaled data/ML platforms with Airflow on GCP (1M+ records/day, 99.9% SLA) and deployed multi-component systems with Docker and Kubernetes (GKE), using modern LLM tooling (LangChain/CrewAI, Claude/OpenAI, Pinecone/ChromaDB, Bedrock/Ollama).”

“GenAI/LLM engineer and architect who built and deployed a production generative AI financial forecasting and scenario analysis platform at McKinsey, leveraging Claude (Anthropic), LangChain, Airflow, MLflow, and AWS SageMaker. Demonstrates strong LLMOps/MLOps rigor (monitoring, drift detection, automated retraining) and deep experience implementing global privacy controls (GDPR, differential privacy, audit trails) while partnering closely with finance executives and legal/IT stakeholders.”

“LLM/document AI engineer who owned a production-grade contract extraction pipeline at CORAMA.AI, ingesting PDFs and dynamic JavaScript sites from 1,000+ government sources. Built a hybrid deterministic+LLM system with two-phase prompting, Pydantic guardrails, confidence scoring, and human-in-the-loop review—cutting error rates from ~35% to <5% and processing 50k+ documents at ~95% accuracy. Also built clinician-in-the-loop orchestration in research, reducing manual labeling time from 3–4 hours to ~50 minutes.”

“ML/NLP engineer focused on production GenAI and data linking systems: built a large-scale RAG pipeline over millions of support docs using LangChain/Pinecone and added a LangGraph-based validation layer to cut hallucinations ~40%. Also built scalable PySpark entity resolution (95%+ accuracy) and fine-tuned Sentence-BERT embeddings with contrastive learning for ~30% relevance lift, with strong CI/CD and observability practices (OpenTelemetry, Prometheus/Grafana).”

“Built a production LLM/RAG-based “model excellence scoring” system at Uber to automatically evaluate hundreds of ML models, standardizing quality assessment and cutting evaluation time from days to minutes on GCP. Also delivered an NLP document classification solution for insurance claims at Globe Life, partnering closely with compliance/operations and improving routing accuracy from ~85% manual to 93% with the model.”

“Built and deployed a production healthcare medical review agent that automates call-transcript summarization and medication reconciliation using a hybrid deterministic + LangGraph-orchestrated LLM workflow. Demonstrates strong reliability engineering (guardrails, schema validation, confidence thresholds, golden/adversarial eval, Langfuse monitoring) in a regulated environment, delivering 60% lower latency and 70%+ efficiency gains while partnering closely with care managers and operations.”

“Built and productionized a RAG-based analytics Q&A assistant for a financial analytics team, enabling natural-language querying across 200+ datasets (SQL tables, PDFs, compliance docs, wikis) and cutting turnaround time by 60%. Deep experience delivering regulated, audit-ready LLM systems on Azure (Azure OpenAI + LangChain) with strict grounding/citations, hybrid retrieval, and AKS-based low-latency deployment, plus strong collaboration with compliance analysts and auditors via iterative Gradio demos.”

“Built and deployed a production RAG-based multi-agent chatbot during an internship to help consultants answer client questions and guide users through new IT systems with step-by-step instructions. Demonstrates hands-on experience with LangGraph/LangChain/Google ADK, unstructured document parsing and chunking for RAG, and a reliability-first approach to agent workflows (metrics, fallbacks, human-in-the-loop, guardrails).”

“Built and productionized an LLM-powered clinical assistant at a healthcare startup, re-architecting a prototype into a robust RAG system on AWS with guardrails, citations, monitoring, and automated tests for clinical reliability. Works closely with clinicians to convert workflow feedback into evaluation criteria and iterative system improvements, and has hands-on experience debugging agentic systems in real time (including during live client demos).”

“Built and deployed a production multi-agent RAG system at JPMorgan Chase to automate regulated credit analysis and compliance clause discovery across large internal policy/document libraries. Implemented LangGraph-based supervisor orchestration with structured state management (Azure OpenAI) to support long-running, resumable workflows, plus hybrid retrieval + re-ranking and guardrails for reliability. Strong at evaluation/observability (trace logging, LLM-judge, HITL) and at communicating results to non-technical stakeholders via Power BI embeds and Streamlit prototypes.”

“Built and shipped a production LLM-powered multi-agent RAG system to automate complex internal support workflows, integrating tool execution (SQL/APIs) with validation guardrails to reduce hallucinations. Optimized for real-world latency and cost via model routing, caching, and async parallel tool calls, and enforced reliability with CI-gated golden test sets derived from anonymized production queries.”