Vetted Prompt Engineering 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.”

“LLM engineer who has shipped production AI systems, including an RFP requirements extraction platform (OpenAI o4-mini + Azure AI Search + FastAPI) achieving 90%+ accuracy and ~5x throughput through grounding, structured outputs, parallelization, and caching. Also partnered with legal/compliance stakeholders at Nexteer Automotive to deliver an AI document comparison tool with traceability and confidence indicators, adopted by non-technical users and saving ~2 FTEs of review time.”

“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.”

“Co-founder of an HR tech startup who took an LLM-centered skill intelligence engine from prototype to production to deliver explainable, skill-based resume insights as an alternative to black-box ATS screening. Previously worked in consulting (Deloitte, Stand Up, Brilio), with experience in technical demos/workshops, pre-sales scoping, and supporting large deal cycles (including a ~$1M UK automotive client).”

“Built and shipped a production-grade GenAI Fraud & Compliance Investigation Copilot for a large US bank, integrating OCR docs, structured data, and prior case history to generate grounded, regulator-friendly summaries and red-flag highlights. Demonstrates strong end-to-end LLM systems engineering (LangGraph/LangChain, hybrid retrieval with FAISS+BM25, guardrails/citations, streaming/latency optimization) plus rigorous evaluation and close partnership with compliance stakeholders.”

“Technical Program Manager and one of three internal "founders" of HP’s Hardware-as-a-Service home printer subscription initiative, spanning DTC shipping, simplified setup, ink replenishment, and warranty coverage. Led roadmap definition/execution, scaled delivery rapidly to 15+ scrum teams (including vendors) with SAFe-like coordination and shared engineering standards, and drove key architecture trade-offs (building an interim subscription management service) to accelerate time-to-market by about a year.”

“React-focused engineer who built a full-stack analytics/test-metrics dashboard (React frontend + Python backend) and turned common UI pieces (data tables, filter panels, chart wrappers) into a reusable internal component library with docs, examples, and basic tests. Strong on profiling-driven performance optimization (React Profiler, memoization) and on owning ambiguous internal-tool projects end-to-end; now planning to package internal patterns into public open-source components.”

“Built and deployed a production generative AI chatbot at NVIDIA using LangChain + GPT-3 integrated with internal data sources, cutting response time nearly in half and improving CSAT by ~12 points. Also delivered LLM-driven QA tools by fine-tuning Hugging Face transformer models and deploying via an AWS-based pipeline (Lambda/Glue/S3) with orchestration (Airflow/Step Functions), CI/CD, Kubernetes, and monitoring (MLflow/Splunk/Power BI).”

“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).”

“Lead Data Scientist (10+ years) with recent work in healthcare data: built production pipelines that unify EHR, genomics, and clinical notes using NLP (spaCy/BERT/BioBERT) and scalable Spark-based processing. Also led development of domain-specific LLM/NLP systems for chatbots and semantic search, deploying models via FastAPI/Flask and improving retrieval with FAISS-backed, fine-tuned clinical embeddings and RAG-style workflows.”

“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).”

“Appian consultant/engineer focused on insurance and financial services modernization and AI-enabled workflows. Built and productionized an AI-driven insurance submission intake system (email ingestion, classification/extraction, HITL review) cutting processing time from 2+ hours to under 10 minutes, and delivered semantic smart search with guardrails and UAT-driven ranking improvements. Also partnered with a global bank CTO org, running sessions with 200+ senior leaders to automate regulatory/board metric reporting via platform integrations and attestation.”

“Backend/product engineer with Tesla experience building and operating a real-time OTA update monitoring and fleet analytics platform at massive scale (telemetry from 3M+ vehicles). Delivered end-to-end systems across Kafka-based ingestion, TimescaleDB/Postgres analytics modeling, FastAPI/GraphQL APIs, and React/TypeScript dashboards, and handled production scaling incidents on AWS EKS during major rollout spikes.”

“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.”