Vetted Large Language Models (LLMs) Professionals

“Backend/platform engineer who has owned a Python/FastAPI results API and deployed it on Kubernetes with Helm and GitHub Actions-driven CI/CD. Demonstrates strong production operations mindset across performance tuning, monitoring, safe rollouts/rollbacks, and phased migrations, plus hands-on Kafka streaming experience focused on ordering and idempotency.”

“AI/ML engineer with CVS Health experience deploying production LLM systems in regulated healthcare settings, including a large-scale RAG solution (1M+ documents) built for compliance-grade, auditable policy/regulatory Q&A with strong anti-hallucination controls. Also delivered an NLP summarization system for physician notes/case narratives by partnering closely with non-technical care operations stakeholders and iterating via prototypes, dashboards, and feedback loops.”

“Data engineering / ML practitioner with experience at MetLife building transformer-based sentiment analysis over large unstructured datasets and productionizing pipelines with Airflow/PySpark/Hadoop (reported 52% efficiency gain). Also implemented embedding-based semantic search using Pinecone/Weaviate to improve retrieval relevance and enable RAG for customer support and document matching use cases.”

“Built a secure, on-prem/private GPT assistant to replace manual SharePoint-style search across thousands of policies/SOPs/engineering docs, using a production RAG stack (LangChain/LangGraph, FAISS/Chroma, PyMuPDF+OCR, vLLM). Implemented layout-aware ingestion (including table-to-JSON) and a multi-agent retrieval/generation/verification workflow with strong observability and compliance guardrails, delivering ~70% reduction in search time.”

“Full-stack engineer who shipped a production "Financial Insight" assistant dashboard in Next.js App Router/TypeScript, integrating a RAG pipeline (embeddings + ChromaDB + LLM) via route handlers and owning post-launch performance (latency, token cost, retrieval relevance). Also built/optimized Postgres-backed workflows for an outbound dialer and callback routing engine handling ~10,000 daily contacts, validating query performance with EXPLAIN (ANALYZE, BUFFERS).”

“Full-stack engineer who built a cloud storage app feature (file upload/management) with Next.js App Router + TypeScript and owned post-launch improvements. Also has internship experience building a geospatial AI chatbot: designed Postgres/PostGIS data models and optimized spatial queries, and implemented an LLM workflow orchestrated with LangChain/LangGraph plus a RAG pipeline grounded in OpenStreetMap data to reduce hallucinations.”

“UK-based Unity developer who built a 3D simulation/digital-twin platform for an autonomous-vehicle startup, integrating Unity environments with external robotics stacks, web APIs, virtual sensing, and dynamic traffic systems. Interested in moving into VR, though has not shipped VR/Meta Quest titles yet.”

“LLM/agent builder who has shipped production AI systems in the wellness space, including an LLM-powered food tracking product used by 5000+ users and a voice/call-routing onboarding workflow using LangGraph/LangChain with LiveKit and Twilio. Strong focus on practical reliability work: latency reduction, retrieval/embedding tuning, and CI-driven evaluation with simulations and metrics.”

“Software engineer with experience across data engineering and backend/platform work: owned a Databricks/PySpark real-time pipeline powering customer dashboards with a 15-minute SLA, and helped modernize an investor web app from JSP to React/TypeScript with API + SQL/materialized-view performance improvements. Also contributed to breaking a Java monolith into microservices (Redis + gRPC on AWS EKS) and built an EC2-deployed Play Store/App Store crawler that reduced third-party data costs.”

“Backend engineer focused on AI-enabled systems, having built a production-style RAG pipeline (vector search + LLM) exposed via Python/Flask endpoints with strong observability and hallucination-reduction techniques. Demonstrates deep performance work in PostgreSQL/SQLAlchemy (5x faster analytics queries) and high-throughput optimization using Celery + Redis (800ms to 120ms latency, 3x throughput), plus schema-per-tenant multi-tenancy with tenant-aware middleware and logging.”

“Built a college AI chatbot during a master’s program, owning the full Python/Flask backend plus Google Gemini integration and a Postgres persistence layer (course info + conversation history), including caching/performance tuning. Also deployed and migrated ETL/ELT workloads from AWS Lambda into Kubernetes/EKS with GitHub Actions-based GitOps CI/CD, IRSA permissions, and Secrets Manager/S3/Postgres connectivity.”

“Built and productionized a recommendation system from notebook prototype into a low-latency, scalable Cloud Run service using Docker, FastAPI, Terraform, CI/CD (GitHub Actions), and MLOps tooling (Vertex AI, MLflow). Experienced diagnosing real-time workflow issues using structured logging/ELK and GCP metrics, including resolving intermittent 504s by fixing unbounded SQL and adding caching. Also partners with sales/customer teams (Wasabi) to deliver tailored demos, troubleshoot, and drive onboarding/adoption.”

“JavaScript/Node.js engineer with open-source contribution experience (Mongoose) focused on connection pooling, test reliability, and memory/resource management. Has diagnosed and fixed real-world performance issues in an insurance claims application and improved resilience via failover DB design. Also experienced producing compliance/governance documentation for an EU-based biopharma, enabling stakeholders to make decisions quickly amid changing regulations.”

“Backend/microservices engineer (Java/Spring Boot, Kafka, Angular microfrontends) with Teradata experience building distributed analytics/query routing platforms and delivering 20–30% latency reductions through event-driven redesign and reliability hardening. Also built and shipped an end-to-end multimodal medical imaging AI feature (LLaVA/Mistral 7B + LoRA) with production guardrails like confidence-based human review, drift monitoring, and audit logs.”

“Built and scaled an AI-powered voice/chat patient engagement platform at Penn Medicine from early prototype into production clinical workflows, focusing on latency, edge cases, and user trust. Strong in LLM reliability engineering (structured prompts, validation/fallbacks), real-time troubleshooting with observability, and cross-functional enablement through pilots, demos, and sales/customer partnership.”

“Accenture engineer who owned an insurance e-application end-to-end and drove incremental releases that reduced recurring production issues. Also built a TypeScript/React (Next.js) + NestJS microservices platform using PostgreSQL, Redis, Stripe, and Kafka, with strong focus on decoupling, eventual consistency, and scaling consumers under load. Created a hackathon chat-based internal assistant that used live form context and documentation-grounded answers to help agents resolve customer queries during form filling.”

“Built and deployed a production SecureAIChatBot (RAG-based) for secure internal information retrieval, using embeddings/vector search, GPT models, monitoring, and safety filters. Focused on real-world production challenges like latency and output consistency, applying caching, retrieval scoping, smaller models, and controlled prompting, and used LangChain to orchestrate the end-to-end workflow.”

“BNY Mellon engineer who has built and operated production AI systems end-to-end: a LangChain/Pinecone RAG platform scaled via FastAPI + Kubernetes to 1000 RPM with 99.9% uptime, supported by monitoring and data-drift detection. Also deep in data/infra orchestration (Airflow, Dagster, Terraform on AWS/EMR/EC2), processing 500GB+ daily and delivering measurable reliability and performance gains, plus strong compliance-facing model explainability using SHAP and Tableau.”

“Robotics software/ML engineer who led an end-to-end transformer-based real-time 3D shape prediction system for a tendon-driven continuum robot on a KUKA arm, including ROS2 multi-camera RGB-D data collection, multi-view calibration, and optimized ICP point-cloud registration. Also optimized an online sensing + motion planning loop for robot-assisted surgery using Bayesian Hilbert maps and A* search, and has Gazebo + RL experience for a robotic salamander.”

“Full-stack engineer focused on productionizing LLM applications, including an Android privacy-policy risk summarization app (Kotlin/React Native + FastAPI + Ollama) that cut response times from ~10s to ~5–6s via batching, caching, async, and event-driven architecture. Currently at PRGX building an LLM-based legal contract clause extraction system, partnering closely with legal/procurement SMEs to create schemas, labeled datasets, and evaluation pipelines that improved accuracy from 70% to 85%. Also has experience architecting real-time voice/LLM systems with streaming microservices (Kafka, Kubernetes, gRPC/WebSockets) and an avatar chatbot pipeline (TalkingHead, Google TTS, AnythingLLM).”