Vetted Large Language Models (LLMs) Professionals

“AI/back-end engineer at Canon who helped build and operate an internal production LLM platform that acts as a secure middle layer between users and models, defending against jailbreaks/prompt injection while enabling RAG, memory, and grounded responses over company data. Experienced with LangChain/LangGraph orchestration, vector DB retrieval, and reliability practices (testing, monitoring, adversarial prompts) to run high-throughput, low-latency AI workflows in production.”

“LLM/AI engineer who has deployed production RAG conversational analytics and Text-to-SQL systems over Snowflake and curated data marts, emphasizing enterprise-grade guardrails for accuracy, security, and cost. Notable for a structured approach to reducing hallucinations (curated metric/table registry, SQL validation, RBAC, and citation-backed responses) and for building resilient, observable multi-step agent workflows using LangChain/LlamaIndex and Airflow.”

“Currently at Uplift AI shipping production LLM features that generate personalized growth insights from user reflections using BERT + embeddings + RAG, with strong safety/guardrail practices for sensitive contexts. Also built an end-to-end React Native UGC challenge submission/moderation system that improved repeat submissions and 7-day retention, and has applied rigorous clinical-style evaluation methods on a dental X-ray disease detection project to reduce false negatives.”

“Built and deployed a production LLM-assisted recommendation and insights platform that unifies structured, semi-structured, and unstructured data via a modular ingestion pipeline, canonical schemas, embeddings, and late-fusion modeling. Experienced in operationalizing ML/LLM systems with Airflow and Kubernetes (Dockerized services, autoscaling, rolling updates) and emphasizes reliability through layered testing, guardrails, monitoring, and A/B experimentation while partnering closely with non-technical stakeholders.”

“Backend/full-stack engineer with experience building customer-facing contact-center automation (agent assignment) and internal editorial/data operations APIs for life-sciences ontology management. Strong in microservices and event-driven systems (Spring Boot + Kafka), third-party integrations (Genesys/Five9), and pragmatic iteration via MVP scoping, tight stakeholder demos, and observability-focused reliability.”

“Software engineer with enterprise CPQ/CRM/ERP integration experience (Argano) who owned an end-to-end pricing preview capability deployed on AWS Kubernetes with Jenkins CI/CD and full observability (Prometheus/Grafana). Also built an AI-native research agent using LangChain + Chroma to filter academic papers, reporting ~15 hours/week saved for a professor.”

“Built a real-time AI meeting assistant using a Chrome extension that streams audio to a backend LLM workflow with transcription and RAG, then hardened it for production with queue-based streaming, async pipelines, security controls, and full observability. Also has hands-on startup sales experience, partnering with customers to define measurable technical win conditions (latency/accuracy) to close deals and drive adoption.”

“Internship at Discovery Education building a production LLM/RAG chatbot that let marketing and sales teams query and interpret Looker/BI dashboards in natural language, with responses grounded in compliance and state education standards. Emphasizes rigorous evaluation (faithfulness/precision/recall/latency) plus user-feedback analytics, and used LangChain for orchestration, chunking/context-window control, and integration with enterprise sources like SharePoint.”

“Data scientist who has built production LLM agents (GPT-4o + LangChain + RAG) to automate analyst-style ad hoc CSV analysis with guardrails and GPT-as-a-judge evaluation. Also delivered an explainable healthcare NLP system for ICD code classification by collaborating closely with clinicians, using a hybrid rule-based decision tree + BERT model to reach 97% accuracy and cut manual review time.”

“Startup-focused builder who created and iterated an MVP for Enky, a two-sided marketplace connecting music artists and creators, informed by hundreds of customer interviews. Implemented CI/CD, monitoring (PostHog/Sentry), and a complex payout pipeline involving scraping social platforms and routing escrow payments via Stripe, and has a track record of quickly debugging production issues (e.g., iOS-specific OAuth cookie failures).”

“Built and shipped a production AI/LLM agent-based, event-driven credit underwriting/decisioning workflow that automated document understanding, retrieval, risk scoring, and compliance checks—cutting turnaround from ~90 days to ~5 minutes while boosting throughput 200x+ and approvals ~50%. Experienced with Airflow/Prefect orchestration, Redis/RabbitMQ queues, rigorous eval/monitoring, and close collaboration with non-technical underwriting teams.”

“Startup-style full-stack/backend engineer with hands-on AWS architecture experience who shipped an LLM-driven assessment-question automation feature (Python microservice calling AWS Bedrock via SQS, deployed on Lambda) with strong validation/guardrails and retry strategies. Also improved production scalability by moving a CPU/IO-heavy file upload path out of a Go API into a queue/Lambda design monitored with CloudWatch, and has React+TypeScript experience optimizing analytics dashboards.”

“Built and shipped a production multi-agent LLM system at Solena that automated internal project intake, validation, reporting, and stakeholder communications using Python, SQL, and LangChain, with strong emphasis on reliability (structured validation, safe defaults, logging, and state tracking). Also used LangGraph to orchestrate a multi-step video summarization pipeline, and has experience partnering with non-technical stakeholders to define “completion” criteria and reporting needs.”

“Built and deployed a production RAG-based multilingual analytics assistant for healthcare operations, enabling non-technical teams to query claims/EHR and risk metrics with grounded explanations. Demonstrates strong end-to-end LLM system engineering (retrieval tuning, re-ranking, hallucination controls, verification layers) plus workflow orchestration (Airflow/Composer/Step Functions) and stakeholder-driven iteration via prototypes and dashboards.”

“Built and deployed a production RAG-based LLM assistant that answers day-to-day operational questions from internal PDFs/SOPs, with strong emphasis on data consistency (metadata versioning, confidence thresholds, conflict handling) and low-latency retrieval at scale. Experienced designing and orchestrating multi-agent LLM workflows (retrieval/validation/generation) and pipeline orchestration for ingestion/embedding/vector-store updates, plus iterative delivery with non-technical operations/business stakeholders.”

“Built and deployed production RAG-based document search/Q&A systems (DocChat and an internship marketing RAG), using a React + FastAPI stack on GCP with docs stored in GCP buckets and retrieval via embeddings/vector DB. Emphasizes cost/performance tradeoffs (reported ~40% cost reduction) and ships via Docker (Railway), with load/API testing using JMeter and Swagger; regularly collaborates with a CEO stakeholder to iterate and push changes to production.”

“Built and operated large-scale biodiversity/ecological research platforms, integrating 50+ heterogeneous global datasets into a unified BIEN 3 schema on PostgreSQL/PostGIS and improving data consistency by 35%. Strong production engineering background (Linux monitoring, CI/CD performance gates, Docker on AWS/Azure) plus applied AI work building a Python RAG system (0.90 precision) and halving latency with Elasticsearch.”

“Robotics/SLAM-focused engineer who worked on RT-Appearance mapping using NetVLAD, replacing traditional CV feature extraction with a deep learning approach to improve loop closure in repetitive green environments. Has hands-on ROS1/ROS2 experience (including bridging), point-cloud alignment with G-ICP for sensor-parameter matching, and Gazebo+Docker simulation testing for motion planning/perception.”

“Built and deployed a production generative-AI copilot at Tungsten that automates invoice/form extraction template creation, reducing weeks of manual model-building work. Combines fine-tuned LLMs (PyTorch/HuggingFace) with OpenCV layout grounding to reduce hallucinations, and runs an end-to-end Kubeflow-based MLOps pipeline with drift monitoring, canary releases, and automated retraining.”

“Founder building SmartTreeQuote, an AI-powered quoting and lead qualification platform for tree service companies, created from direct operator pain points and validated via an image-based remote quoting workflow. Has already built a targeted customer acquisition motion resulting in 5 active paying subscribers, and brings experience proposing AI-driven workflow automation to remove operational bottlenecks in a high-volume install environment.”

“Backend/platform engineer who built and evolved a large-scale healthcare document processing system (OCR + LLM orchestration) in Python/FastAPI on Google Cloud (Cloud Run, GCS, Firestore), processing ~1.5M files per batch and tens of millions overall. Emphasizes reliability and operational safety via deterministic IDs, idempotent state machines, strong observability, and self-healing reconciliation, plus disciplined migrations using dual-run validation and incremental rollouts.”