Vetted Vertex AI Professionals

“LLM/agentic-systems engineer with PayPal experience hardening an LLM-powered fraud support assistant from prototype to production, focusing on low-latency distributed architecture, rigorous evaluation/testing, and security/compliance. Comfortable in customer-facing and GTM contexts—runs technical demos/workshops, builds tailored pilots, and aligns sales/CS with engineering to close deals and drive adoption.”

“Built and owned an end-to-end AV operations automation and dashboarding platform for USC event operations, used daily to coordinate hundreds of live events. Delivered a React/TypeScript full-stack system integrating Smartsheet APIs with strong reliability practices (typed contracts, validation/fallbacks, safe rollouts) and experience with queue-based microservice patterns (idempotency, retries, DLQs, monitoring).”

“Built and deployed a production Retrieval-Augmented Generation (RAG) platform in a healthcare setting to automate clinical documentation review and summarization, targeting near-real-time, explainable outputs. Emphasizes grounded generation to reduce hallucinations, latency optimizations (chunking/embedding reuse), and PHI-safe workflows with access controls, plus strong orchestration experience using Apache Airflow.”

“Building a production LLM/agent system for a leading US dental provider that extracts rules from payer handbooks/portals and EDI 271 responses to validate and improve patient cost estimates. Combines GCP stack (BigQuery, GKE, Cloud Run, Pub/Sub, Vertex AI) with strong agent reliability practices (observability, validator agents, grounding, PII/hallucination guardrails, confidence scoring) and has led non-technical customer stakeholders on enterprise ServiceNow↔Aha sync and AI-powered enterprise search/summarization.”

“Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).”

“Built and shipped a real-time oncology risk prediction system used by doctors during patient visits, trained on clinical data in AWS SageMaker and deployed via FastAPI with sub-second responses. Emphasizes clinician-trust features (SHAP explainability, validation checks) and HIPAA-compliant controls (encryption, RBAC, audit logging), plus Kubernetes-based production operations with autoscaling, monitoring, and drift/retraining workflows; collaborated closely with oncologists at Flatiron Health.”

“Built a production RAG-based GenAI copilot backend at Aetna using Python/FastAPI, GPT-4, LangChain, and Azure AI Search, deployed on AKS with Prometheus/Grafana observability. Owned the system end-to-end (ingestion through deployment) and improved peak-time reliability by addressing vector search and embedding bottlenecks with Redis caching, index optimization, and async processing, plus added anti-hallucination guardrails via retrieval confidence thresholds.”

“LLM engineer who built a production seller-support RAG system at eBay using hybrid retrieval (BM25 + Pinecone vectors) with Cohere reranking, LangGraph orchestration, and citation-grounded answers. Strong focus on reliability: semantic/structure-aware chunking, automated Ragas-based evaluation with nightly regressions, and production observability (LangSmith) plus drift monitoring (Arize). Also implemented a multi-agent fraud pipeline with AutoGen using JSON-schema contracts and explicit termination conditions.”

“AI/ML practitioner with Northern Trust experience who has shipped production LLM systems (internal support assistant) using RAG, vector databases, orchestration (LangChain/custom pipelines), and rigorous monitoring/feedback loops. Also built AI-driven fraud detection/risk monitoring solutions in a regulated financial environment, emphasizing explainability (SHAP), audit readiness, and stakeholder trust through dashboards and clear communication.”

“Full-stack developer (Energywell) who led an internal admin dashboard end-to-end using React/Redux and a Go microservice, emphasizing performance (reduced calls, preload data) and maintainable architecture (modularity, refactoring, PR reviews). Also shipped a Redis-based caching whitelist feature in a fast-paced environment and helped implement a responsive, brand-configurable onboarding/signup frontend.”

“Enterprise platform engineer/product owner who led end-to-end delivery of customer-facing ServiceNow Service Catalog/workflow solutions, emphasizing reliability, security, and fast iteration. Built React/TypeScript portals with Node.js and Spring Boot backends, and improved microservices reliability at scale using Kafka, monitoring, and robust retry/timeout patterns.”