Vetted Google Cloud Platform Professionals

“Engineer with hands-on experience embedding AI into software delivery workflows, including Claude-powered PR review, testing, debugging, and multi-agent coding pipelines. They pair AI automation with strong systems thinking around microservices, fault tolerance, multi-AZ design, caching, and security controls like WAF and rate limiting, and also experiment independently with RAG and multi-agent search projects.”

“Engineer with hands-on experience owning customer deployments for ordering and billing systems at Amdocs, including performance tuning, CI/CD improvements, and post-launch stabilization that delivered about 50% faster execution time. Also built and debugged an LLM-powered task prioritization app using Gemini, Streamlit, Python, and MongoDB, with a strong focus on prompt reliability, validation, and handling inconsistent real-world inputs.”

Kevin Delong

Screened

Senior AI/ML Engineer specializing in Generative AI, LLMs, and RAG systems

Irvine, CA12y exp

“AI/ML engineer with hands-on experience shipping production systems across fintech, travel, and legal use cases. They’ve built end-to-end chatbot, generative content, and RAG solutions on AWS with CI/CD, monitoring, and guardrails, including a loan application platform that generated $3,000 in sales in its first month.”

Nathan Holmes-King

Screened

Junior Data Scientist and Full-Stack Developer specializing in healthcare and matching systems

Iceland4y exp

“Solo builder developing their own app and using generative AI tools like Codex to both troubleshoot and write code. They taught themselves React and React Native over the past year through hands-on AI-assisted development and handle all technical work independently.”

“Robotics/embedded candidate who built an IoT smart shoe for visually impaired users, implementing real-time obstacle detection with ultrasonic sensors and haptic feedback on Arduino. Has practical ROS experience (RViz/Gazebo) and improved reliability in distributed systems by hardening an Arduino-to-ROS serial protocol with framing, strict parsing, and sensor-noise filtering; also containerized ROS environments with Docker for reproducible simulation and onboarding.”

“Backend/data engineer in healthcare who built an AWS-based clinical analytics platform from scratch (DynamoDB/S3/Airflow/dbt) with sub-second clinician query goals, 99.9% uptime, and HIPAA-grade controls (KMS encryption, IAM RBAC, audit trails). Also modernized ML delivery by replacing a manual 4-hour deployment with a 30-minute Docker/GitHub Actions CI/CD pipeline using parallel runs, parity testing, and rollback, and caught critical EHR data edge cases (date formats/timezones) that could have impacted patient care.”

“Co-op engineer at EnFi who built and maintained a multi-tenant prompt library and LLM workflow tooling used by internal teams and external enterprise clients. Led TypeScript/React package design and standardized a typed workflow abstraction across disparate implementations (React, Go, JSON), improving reliability and developer adoption. Delivered measurable performance gains (~25% latency reduction) and owned end-to-end execution including docs, demos, debugging, and deployment.”

“Backend/data engineer who built a production-ready multi-agent financial intelligence system (Mycroft) that orchestrates specialized AI agents to analyze real-time market data using FastAPI and Pinecone vector search. Brings strong security/reliability instincts (rate limiting, JWT/OAuth2, retries/backoff, health checks) and has caught high-impact data integrity issues in financial migrations (timezone normalization across global legacy systems).”

“Backend engineer who has built end-to-end data and platform systems across domains: a Scala/Java media data warehouse with a custom query language and Elasticsearch search, plus production security patterns (RBAC, RLS, audit trails) including a telehealth platform. Also demonstrated strong operational rigor by using feature-flagged side-by-side migrations and by catching ecommerce checkout edge cases that were dropping revenue.”

“JavaScript engineer and open-source contributor focused on runtime performance, reliability, and developer experience—refactored a widely used client-side API/state library to improve concurrent request handling, error consistency, and UI performance while adding tests and documentation. Also owned improvements to a core microservice at Velsa integrating multiple hospital systems, bringing structure to ambiguous priorities and delivering stability and performance gains from design through deployment.”

“Built a production GPT-4/LangChain/Pinecone RAG “AI Copilot” at Northern Trust to automate financial report generation and analyst Q&A over internal structured (SQL warehouse) and unstructured policy data. Focused on real-world production challenges—grounding and latency—achieving major speed gains (seconds to milliseconds) via MiniLM embedding optimization and Redis caching, and implemented rigorous testing/evaluation with MLflow-backed metrics while aligning compliance and finance stakeholders for deployment.”

“Forward-deployed, full-stack/platform engineer who owns production features end-to-end across frontend, backend, data, and infrastructure (AWS serverless, Terraform, React). Has modernized critical fintech/payment systems (zero-downtime monolith-to-microservices with Kafka event sourcing) and productionized AI-native support workflows (LLM + RAG on Pinecone) with measurable gains in latency, incidents, CSAT, and support efficiency.”

“ML/LLM engineer with production experience building medical RAG systems to automate chart review, including retrieval + re-ranking and rigorous evaluation. Notably uncovered errors/bias in physician-curated ground truth by tracing answers back to source note chunks and presented evidence to an academic partner, accelerating deployment. Also built a RAG-based FAQ chatbot for a health insurance company and delivered it to non-technical stakeholders via demos.”

“Backend/platform engineer with hands-on ownership of Kubernetes GitOps delivery (GitHub Actions + Argo CD) on AWS EKS, including progressive rollouts and reliable rollback across interdependent microservices. Built a Python/FastAPI ML-driven document-processing service (PostgreSQL + S3) to complement existing Spring Boot systems, and implemented Kafka streaming pipelines with Schema Registry plus Prometheus/Grafana observability. Also supported a hybrid cloud-to-on-prem migration for compliance/latency with phased rollout and incremental PostgreSQL migration.”

“Built and shipped “LabMate,” a production AI assistant specialized in laboratory hardware, using a weighted multi-source RAG pipeline with reranking and reasoning-focused query decomposition to handle complex user questions. Deployed on a local GPU cluster with vLLM and NVIDIA MPS (plus OCR/VLM components), and established evaluation using synthetic + public reasoning datasets while collaborating weekly with non-technical admins to align requirements and resource constraints.”

“Built and deployed a production RAG-based enterprise document intelligence platform for financial/compliance/operational documents on AWS (Spark/Glue ingestion, embeddings + vector DB, LangChain orchestration, REST APIs on Docker/Kubernetes). Deep hands-on experience orchestrating multi-step and multi-agent LLM workflows (LangChain, LangGraph, CrewAI) with strong focus on grounding, evaluation, observability, and cost/latency optimization, and has partnered closely with non-technical finance/compliance teams to drive adoption.”

“Clinical data scientist who has built an agentic LLM-powered literature review assistant (with RAG-style storage/retrieval) to identify predictors for downstream predictive modeling. Also delivered a patient-focused progression analysis model using Databricks + Airflow orchestration, partnering closely with clinicians to define targets and validate that model insights aligned with clinical expectations.”

“ML/AI engineer with production experience in high-scale banking fraud detection at Truist, building an end-to-end pipeline (Airflow/AWS Glue/Snowflake, PyTorch/sklearn) with automated retraining and Kubernetes-based deployment; delivered measurable gains (22% fewer false positives, 15% higher recall) and reduced manual ops ~40%. Also partnered with clinicians at Kellton to deploy an LLM system for summarizing/classifying clinical notes, improving review time and decision speed.”

“ML/NLP engineer with hands-on experience building production systems for unstructured insurance claims and customer data linking. Delivered measurable impact at scale (millions of documents), combining transformer-based NLP, vector search (FAISS/Pinecone), and human-in-the-loop validation, and has strong production workflow/observability practices (Airflow, AWS Batch, Grafana/Prometheus).”

“ML/NLP engineer (Beyond Identity) who built production semantic search and entity-resolution systems over internal security documentation, using LDA + BERT embeddings with FAISS/Pinecone to cut search time by 30%. Also scaled a real-time anomaly detection pipeline to millions of events/day with Spark and AWS Lambda, with strong emphasis on measurable validation (Precision@k, MRR, F1, ARI).”

“Built and deployed a production Llama 3-based RAG document Q&A system using FAISS, addressing context-window limits through chunking and keeping retrieval accurate by regularly refreshing embeddings. Has hands-on orchestration experience with LangChain and LlamaIndex for multi-step LLM workflows (including memory management) and collaborates with non-technical teams (e.g., marketing) to deliver AI solutions like recommendation systems.”

“Infrastructure/DevOps engineer (Geico) focused on AWS and Kubernetes at production scale. Has hands-on experience building secure GitHub Actions CI/CD for EKS, provisioning core AWS infrastructure with Terraform/CDK, and leading end-to-end incident response with post-incident automation to prevent recurrence; no direct IBM Power/AIX/PowerHA experience.”

“Backend engineer who has shipped both high-throughput real-time systems and production LLM/RAG features. Built a database-free, local-first messaging service (Node/Express/Socket.IO) achieving ~1,500 msgs/sec at <25ms p95, and implemented a Go-based RAG recommendation pipeline with strict JSON/schema validation, catalog grounding, fallbacks, and eval loops that cut hallucinations to ~1–2% while reducing LLM costs ~60%.”

“Senior technologist (25 years experience) who served as chief architect/CTO for a patented software startup that was acquired. Strong at building scalable, robust, technology-agnostic systems and translating technical value into investor-ready narratives (forecasts, roadmaps, documentation). Currently prefers joining an existing founding team as a key technical leader/mentor rather than leading entrepreneurship solo.”