Vetted Vertex AI Professionals

“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.”

“ML/NLP engineer focused on real-time IT ops analytics, building a predictive maintenance/anomaly detection platform end-to-end (multi-source ETL, streaming, modeling, and production deployment on GCP/Vertex AI). Uses deep learning (LSTMs, autoencoders/VAEs) plus embeddings (SentenceBERT) and vector search to improve incident correlation and search, citing ~40% reduction in duplicate alert noise.”

“Built production LLM + hybrid RAG and multi-agent orchestration systems at Wells Fargo to automate complaint document/audio transcript understanding and categorization, addressing vocabulary drift via embedding + vector index updates instead of frequent retraining. Strong in LLM workflow reliability (testing/benchmarks/observability) and stakeholder-facing delivery with explainability (citations/SHAP-style justifications) and Tableau dashboards.”

“Former co-founder at PreConception (acquired) who partnered closely with Operations, Legal, and Medical teams to deliver a HIPAA-compliant product meeting technical and regulatory requirements. Motivated by mission and team fit, and interested in a Venture Studio CTO path with a focus on 0-to-1 building and early validation via beta testing/PMF.”

“Built and shipped a production RAG-based enterprise knowledge assistant to replace slow/inaccurate search across millions of documents, using LangChain orchestration with GPT-4/LLaMA and vector databases. Strong focus on production constraints—latency, hallucination control, and cost—using hybrid retrieval, guardrails, LLM-as-judge validation, and model routing, and has experience translating non-technical stakeholder pain points into measurable outcomes.”

“AI engineer with production experience building multilingual speech-to-speech translation pipelines (ASR + LLM) for enterprise/media, focused on reliability at scale. Has hands-on orchestration experience (including IBM Watson contexts) and emphasizes production evaluation/monitoring using a mix of traditional metrics and LLM-based evaluators to catch quality regressions while balancing latency and cost.”

“Built and deployed an LLM-powered claims-document summarization system (insurance domain) that cut agent review time from 4–5 minutes to under 2 minutes and saved 1,200+ hours per quarter. Hands-on across orchestration and production infrastructure (Airflow retraining DAGs, Kubernetes, SageMaker endpoints, FastAPI) and recent RAG workflows using n8n + Pinecone, with a strong focus on reliability, cost, and explainability for non-technical stakeholders.”

“Engineering/product leader who builds business-aligned technology roadmaps and scales pod-based orgs with strong delivery discipline (OKRs, CI/CD, QA automation). Led a SaaS supply-chain application adopted by Fortune 100 customers, citing ~$4M MRR and ~87% gross profit, and has hands-on experience standardizing LLM + cloud/MLOps architectures with security/compliance guardrails. Also created the PISEK methodology and used it to run distributed innovation sprints (e.g., an AI ETA predictor moved from pilot to production).”

“Healthcare-focused LLM engineer who deployed a production triage and clinical knowledge retrieval assistant using RAG and LangGraph-orchestrated multi-agent workflows. Emphasizes clinical safety and compliance with robust hallucination controls, HIPAA/PHI protections (tokenization, encryption, audit logging, zero-retention), and human-in-the-loop escalation; reports a 75% latency reduction in a healthcare agent system.”

“Built a production multi-agent orchestration platform to automate healthcare claims and HR workflows, combining LangChain/CrewAI/AutoGPT with RAG (FAISS/Pinecone) and fine-tuned open-source LLMs (LLaMA/Mistral/Falcon) in private Azure ML environments to meet HIPAA requirements. Emphasizes rigorous agent evaluation/observability (trajectory eval, adversarial testing, LLM-as-judge, drift monitoring) and reports measurable outcomes including 35% faster claims processing and 40% fewer chatbot errors.”

“Full-stack engineer with enterprise experience at Metasystems Inc. (and Qualcomm) building high-traffic, security-sensitive systems—owned a secure transaction processing module end-to-end using Java/Spring Boot, Python/Django, and React. Strong AWS production operations (EKS/ECS/Lambda/RDS/DynamoDB) with IaC (Terraform/CloudFormation), observability, and reliability patterns; also delivered resilient ETL/integration pipelines with idempotency/retries/backfills and achieved a 50% deployment-time reduction through CI/CD and modular refactoring.”

“IBM engineer who built and deployed a production RAG-based LLM assistant using LangChain/FAISS with a fine-tuned LLaMA model, served via FastAPI microservices on Kubernetes, achieving 99%+ uptime. Demonstrates strong practical expertise in reducing hallucinations (semantic chunking + metadata-driven retrieval) and managing latency, plus mature MLOps practices (Airflow/dbt pipelines, MLflow tracking, monitoring, A/B and shadow deployments) and effective collaboration with non-technical stakeholders.”

“Built and deployed a production LLM-powered RAG assistant for healthcare teams (care managers/support) to answer questions from clinical and policy documentation, emphasizing trustworthiness via improved retrieval, reranking, and strict grounding prompts to reduce hallucinations. Also has hands-on orchestration experience with Apache Airflow for end-to-end ETL/ML workflows and applies rigorous testing/metrics (hallucination rate, tool-call accuracy, latency, cost) to ensure reliable AI agent behavior.”

“LLM/agentic systems engineer who built a production "Agentic AI Diagnostic Assistant" for network engineers, using a multi-agent Llama 2 + LangChain architecture with RAG over telemetry/incident data in DynamoDB and confidence-based deferrals to reduce hallucinations. Also has strong MLOps/orchestration experience (Airflow, EventBridge, Spark, Docker, SageMaker/ECS) at multi-terabyte/day scale and delivered multilingual NLP analytics (fine-tuned BERT/spaCy) for support operations through hands-on stakeholder workshops.”

“Built a production multi-agent LLM system at Live Nation on Databricks (LangGraph/LangChain) that let venue/event teams ask questions in Slack, auto-generated optimized route schedules, and produced inventory/stocking recommendations from historical SQL data and venue trends. Improved reliability by tightening prompts with strict JSON schemas, providing sample questions/SQL, and adding guardrails plus synthetic/edge-case testing, while iterating with event managers and senior VPs via prototypes and feedback loops.”

“ML/NLP practitioner with banking experience (M&T Bank) who has built a GPT-4 RAG system using LangChain and Pinecone to connect unstructured customer data with internal knowledge bases, improving accuracy and reducing manual lookup time by 50%+. Strong in entity resolution and productionizing scalable Python data workflows, including major performance wins by migrating bottleneck joins from Pandas to Dask.”

“Full-stack TypeScript engineer with early-stage startup experience (HomePulse; sole US engineer) who ships and owns production features end-to-end—routing/state design, API contracts, caching/pagination, and post-launch monitoring/optimization. Has delivered performance-sensitive React UIs (virtualized large datasets, React Query caching, Suspense loading patterns) and built durable job-queue workflows with idempotency/retries, plus SQL Server relational modeling for internal ticketing and knowledge-retrieval workflows.”

“Full-stack engineer with healthcare and finance experience who has owned end-to-end production systems across Azure and AWS. Built a real-time clinical dashboard at Centene (React + FastAPI + Azure Event Hubs) that cut data latency from ~12 minutes to under 1 minute and was associated with a 30% reduction in intervention delays. Also delivered MVPs in high-ambiguity environments at Accenture during monolith-to-microservices migration, improving uptime and maintainability with measurable results.”

“AI/LLM engineer who has shipped production RAG systems (including a 250K-document compliance knowledge tool on AWS) and focuses on reliability via citations, guardrails, and rigorous evaluation (Ragas/Opik/DeepEval). Also built a LangGraph-orchestrated webcrawler agent that cut research paper extraction from hours to minutes, and collaborated with clinical teams to deliver patient volume forecasting with an optimization layer for staffing.”

“Founding/solo engineer who rebuilt an early-stage product from the ground up: Ask NETA, an AI assistant for electricians to answer complex electrical code questions. Delivered a full-stack TypeScript system (React web + React Native iOS/Android, Express API, Postgres on AWS) with CI/CD, observability, and a Vertex AI RAG pipeline, reaching 3,000 MAUs in the first month; also built a real-time distributed scoring system handling unreliable hardware data with sequencing and retries.”