Vetted Google Cloud Platform Professionals

“Cofounded a travel app and built a production place recommendation + review system end-to-end using Next.js App Router and TypeScript, including Postgres-backed APIs and post-launch monitoring. Uses structured logging with Sentry and Vercel Analytics to diagnose issues and validate performance improvements, and has some exposure to Temporal-based workflow orchestration with retries/idempotency.”

“Software engineer with experience across TCS, Rakuten, and USC who has owned production integrations and data pipelines end-to-end. Notably improved a trading platform payment flow by replacing fragile polling with a webhook-driven status system with robust fallbacks, and has shipped LLM-assisted design-to-webpage automation plus evaluation-driven prompt iteration (NYT Connections).”

“Cross-functional product/engineering leader with startup and enterprise experience (Whimstay, Wells Fargo, Leap AI Technologies). Has owned end-to-end delivery including UI and payment architecture—built a multi-customer reservation payment flow using pre-auth plus event/timer-driven reminders and handled tricky edge cases like date changeover. Also introduced lightweight process (Jira, sprints, team/module structuring) to increase delivery speed in an early-stage AI document-matching startup.”

“Full-stack engineer with fintech trading-platform experience who shipped and operated a real-time portfolio P&L/performance feature end-to-end (React + Node/WebSockets + MongoDB) on AWS, including significant performance tuning under peak trading load. Also built a Spark-based trading analytics pipeline with idempotency and reconciliation for auditability, and has a personal React/TS + Node/Express project (Artsy) with JWT auth and schema-evolution practices.”

“Data engineer with hands-on experience migrating a legacy/mainframe-fed loader onto GCP, orchestrating daily SFTP-to-GCS ingestion, Spark/Scala transformations, and loading into Cassandra/Solr/OpenSearch with API- and BigQuery-based validation. Also built a Java Spring Boot service that extracts from Hive and produces Excel outputs, emphasizing testing, logging/alerts, and CI setup.”

“Full-stack engineer who shipped an end-to-end speech capability for an LLM chatbot UI, integrating OpenAI APIs and publishing via Google Apigee with client documentation. Has experience operating deployments with Jenkins/Kubernetes/Docker and monitoring with Datadog, and has worked in an innovation-center environment building rapid prototypes under ambiguity with tight stakeholder feedback loops.”

“Enterprise platform engineering leader who identified platform engineering as a major opportunity at Kyndryl and built an entire internal practice around it by codifying the offering and evangelizing it across leadership. Now exploring founding an agentic AI developer platform aimed at reducing variance and improving consistency in building/deploying cloud-native applications; has not raised capital yet.”

“Insurtech R&D leader turned CTO with 25+ years across telecom, DoD, automotive, and commercial insurance. Built patented AI-driven insurance document ingestion (>95% accuracy with 1–3 samples) and led Azure-based backend/service development while managing up to 15 engineers at a commercial P&C MGA that reached a ~$60M book of business (~30 loss ratio) before run-off due to paper issues. Known for hands-on discovery (including field inspections) and rapid MVP delivery, including a risk-inspection iPad app that auto-generated draft reports.”

“Backend engineer focused on productionizing LLM systems: built a FastAPI-based RAG and multi-agent automation platform deployed with Docker/Kubernetes, prioritizing safe execution and reduced hallucinations. Experienced in refactoring monolithic ML services with feature-flagged incremental rollouts, and implementing JWT/RBAC plus row-level security (e.g., Supabase) for secure, scalable APIs.”

“Built and led an autonomous driving software effort for Formula Student, owning the full autonomy stack (perception, planning, control) orchestrated in ROS. Implemented stereo depth + YOLO object detection, RRT/RRT* planning, and a robust SLAM pipeline (Kalman filter, submapping) while leveraging Gazebo simulation and modern deployment tooling (Docker/Kubernetes, AWS, GitHub Actions CI/CD).”

“Frontend lead who owned architecture and quality for TELUS’s Next Generation Sales Platform, building a modular React+TypeScript experience that scaled across wireline/wireless products and channels. Experienced in hardening UIs against unreliable backend integrations (API abstraction, retries/fallbacks, caching, logging) and delivering real-time dashboards via WebSockets, with strong CI/CD testing and blue-green release practices for high-stakes launches like Black Friday.”

“Built and deployed a production LLM-powered lesson adaptation platform for K–12 educators that personalizes content for multilingual and neurodiverse students using RAG and content transformation. Owned the full stack from FastAPI backend and OpenAI integration through reliability/safety controls, latency/cost optimization, and weekly shippable modular APIs, iterating directly with curriculum stakeholders to reduce hallucinations and improve educator trust.”

“ServiceNow engineer who built an AI-powered ticket summarizer end-to-end (RAG with vector DB + GPT, Redis latency optimizations, fallback summarization, and a React UI widget for agent feedback). Also has hands-on ROS 2 experience building real-time sensor-fusion nodes (LiDAR/IMU), debugging SLAM/navigation issues via rosbag + EKF tuning, and bridging heterogeneous robots by translating ROS 2 topics to MQTT/JSON. Strong DevOps background with Docker, Jenkins CI/CD, and Kubernetes orchestration for scalable deployments.”

“Open-source JavaScript contributor focused on data visualization, extending Chart.js/React with custom plugins for real-time streaming dashboards. Designed an end-to-end telemetry pipeline using Apache Kafka and Azure Cosmos DB, optimizing partitioning, batching, caching, and client throttling to keep latency low and support thousands of concurrent users. Demonstrates strong ownership in fast-changing environments, including building full-stack AI applications and ingestion/ETL pipelines at Robotics Technologies LLC.”

“Full-stack engineer focused on enterprise, cloud-native microservices—building Spring Boot backends and React/Angular front ends with strong security (OAuth/JWT), AWS infrastructure (RDS/S3), and containerized deployments (Docker/Kubernetes). Has delivered data-heavy order/account/transaction platforms and healthcare solutions including EHR integrations for secure patient data exchange, with emphasis on testing, performance tuning, and reliability (load testing).”

“Built and productionized a HIPAA-compliant LLM+RAG Clinical AI assistant at Optum, fine-tuning GPT/LLaMA on de-identified patient notes and integrating FAISS/Pinecone for sub-second retrieval; reported to cut diagnosis time by ~20 minutes per case. Experienced in orchestrating ML pipelines (Airflow, AWS Step Functions, Azure Data Factory) and in reliability techniques for LLM systems (grounding, citations, confidence filters, monitoring) while partnering closely with clinicians and compliance teams.”

“Backend engineer with hands-on experience building secure Python/Flask services (sessions, JWT, RBAC) and optimizing PostgreSQL/SQLAlchemy performance, including custom SQL using CTEs/window functions profiled via EXPLAIN ANALYZE. Also integrates LLM features via OpenAI/Azure into backend systems and improves scalability with RabbitMQ-driven async processing, caching, and multi-tenant data isolation patterns.”

“Built an AI basketball shooting coach that analyzes player form against NBA players and recruited 30+ beta users via Reddit to drive iterative UI/workflow improvements. Also has internship experience building an administrative server and coordinating API/database compatibility with another client server, emphasizing communication and integration quality.”

“AI/ML engineer with healthcare domain depth who led a HIPAA-compliant, production LLM system at McKesson to automate clinical document understanding—extracting entities, summarizing provider notes, and supporting authorization decisions. Hands-on across Spark/Python ETL, Hugging Face + LoRA/QLoRA fine-tuning, RAG, and cloud-native MLOps (Airflow/Kubernetes/Step Functions, MLflow, blue-green on EKS/GKE), with explicit work on PHI handling and hallucination reduction.”

“Built a production LLM-agent framework for a startup that performs daily financial/trading analysis by combining live market data with internal tools, including a centralized memory module to prevent context drift and reduce hallucinations. Also implemented an Airflow-orchestrated retail price forecasting pipeline deployed to AWS endpoints, scaling parallel workloads via Kubernetes Executor and validating systems with rigorous functional + LLM-specific metrics and cross-team collaboration.”

“Backend engineer who has delivered large, measurable performance wins (10x throughput, 67% latency reduction) by combining Flask microservices, Redis caching, and AWS autoscaling/observability. Has hands-on depth in SQLAlchemy/Postgres optimization and production scaling pitfalls (cache consistency, connection exhaustion), plus experience deploying real-time ML inference (XGBoost) on AWS Lambda and building secure multi-tenant Kubernetes isolation.”

“Accenture data/ML practitioner who deployed a retail churn prediction and BERT-based sentiment analysis system to production, integrating behavioral + feedback data and operationalizing it with ETL automation, orchestration, and CI/CD. Experienced managing 2TB+ multi-source data, monitoring drift in Databricks, and translating results into Power BI dashboards for marketing teams (including K-means customer segmentation).”

“ML/NLP engineer who built a production system that converts large-scale unstructured text into a connected, searchable knowledge base using spaCy + Sentence Transformers/FAISS and a Neo4j knowledge graph, with BERTopic and XGBoost for organization/labeling. Strong focus on production-grade Python workflows (FastAPI/Celery, Pydantic validation, Docker, AWS ECS/Lambda) and robust entity resolution with measurable precision/recall and human review for low-confidence matches.”

“Machine Learning Engineer currently at Webster Bank building an enterprise-scale LLM agent for Temenos Journey Manager/Maestro, using RAG-style multi-stage retrieval with FAISS/Pinecone, hybrid dense+sparse search, and LoRA fine-tuning optimized via NDCG/MAP and A/B testing. Previously handled messy incident/telemetry data at Deuta Werke GmbH with deterministic + fuzzy entity resolution, and has strong production data engineering experience across Spark/Hadoop and Python ETL systems.”