Vetted Google Cloud Platform Professionals

“Built and operated a production warehouse metadata collection platform at Sigma Computing, integrating Go/gRPC services with a TypeScript backend and MySQL, with strong emphasis on idempotency, retries, bounded-concurrency job queues, and Datadog-based observability. Also created Kurral (kurral.com), an AI agent runtime security and observability/governance SDK/proxy concept, iterating via pilot-customer feedback and market research; targeting founding engineer roles with $180–200k base and ~2–5% equity.”

“Senior engineering leader with deep hands-on experience building and scaling high-throughput platform infrastructure, including a Qualys core data platform using Kafka, Cassandra, OpenSearch, Ceph, and API gateways. He combines architecture depth with people leadership, and has led complex cross-team initiatives such as rolling out a Kubernetes-based container platform across 20 application teams using a phased migration strategy.”

Manaswini Gogineni

Screened ReferencesStrong rec.

Mid-Level Software Engineer specializing in cloud infrastructure and full-stack web development

San Francisco, CA2y exp

“Backend engineer at Electric Hydrogen who built a serverless device-log ingestion and processing platform in Python/Flask, scaling throughput (4x peak ingestion) while keeping sub-300ms API latency. Strong in Postgres/SQLAlchemy performance (partitioning, materialized views) and production ML integration (ONNX model served via FastAPI microservice with async batch inference, Redis feature caching, and drift monitoring via S3/Lambda). Experienced designing secure multi-tenant systems with schema-per-tenant isolation and KMS-backed encryption.”

“Robotics-focused engineer with ROS 2 experience who has built and debugged real-time, distributed control/orchestration systems under production-like latency and safety constraints. Led platform changes at Persona for a real-time verification orchestration system using deterministic state machines and async workers, and has hands-on experience stabilizing multi-robot navigation/SLAM behavior using rosbag, RViz, and stress testing in simulation (Gazebo).”

Yuan-Hsuan Wen

Screened

Intern Software Engineer specializing in AI agents, RAG pipelines, and semiconductor systems

Taipei, Taiwan3y exp

“Built a web-based interface that connects an internal bug system to an LLM for initial debugging and issue classification, aiming to boost QA and software engineer efficiency while balancing latency and accuracy. Worked as a one-person project and managed constraints like limited hardware and difficulty extracting team debugging context, relying on manager communication and rapid modeling to validate direction.”

Christopher Bun

Screened

Executive AI/ML technology leader specializing in healthcare, biotech, and legal AI

Irvine, CA17y exp

“Repeat founder and startup advisor with experience spanning academic, health tech, legal tech, sports, and gaming. Has participated in fundraising and due diligence and has built companies, engineering teams, and software platforms from scratch, with a strong product-design-first approach to product-market fit and market selection.”

“Full-stack engineer at Vanguard who architected and shipped a production AI onboarding chatbot using React, Python, LangChain, RAG, and AWS Bedrock, reaching 50,000+ prospects in two months and reducing drop-off by 26%. Particularly compelling for teams building regulated AI products: they combine hands-on full-stack delivery with guardrails, observability, and experimentation, and also build consumer AI products on the side in endurance coaching.”

Shailaja Domala

Screened

Executive product and AI leader specializing in data platforms and analytics

California, USA24y exp

“Engineering leader with deep experience at Visa building and modernizing large-scale analytics platforms, including refactoring legacy systems into globally available microservices on AWS. Combines hands-on technical judgment in architecture, search platform evaluation, and service reliability with management of distributed international engineering and data teams.”

“Robotics software engineer with internship experience at John Deere and AeroVironment, working across C++/Python stacks and ROS2-based systems. Drove a proof-of-concept migration from an x86/FPGA target to NVIDIA GPU solutions and helped turn a hackathon prototype into a production-ready, CI/CD-driven build-and-deploy workflow with comprehensive automated testing.”

“Machine learning/NLP practitioner at J.P. Morgan who led development of a production RAG system and an entity resolution pipeline for complex financial data. Deep hands-on experience with embeddings (Sentence-BERT), vector search (FAISS/pgvector), LLM fine-tuning (LoRA/PEFT), and rigorous evaluation (human-in-the-loop + A/B testing) backed by strong MLOps on AWS (Docker/Kubernetes, MLflow, Prometheus/Datadog).”

Nishitha Thummala

Screened

Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable inference

San Francisco, CA6y exp

“Backend/retrieval-focused engineer with production experience at Perplexity building a large-scale real-time Q&A system using retrieval-augmented generation, emphasizing low-latency, high-quality answers through ranking, context optimization, and caching. Also has orchestration experience from both product-facing LLM pipelines and large-scale infrastructure workflows at Meta, and has partnered with non-technical stakeholders to align AI trade-offs with business goals.”

Mitul Sheth

Screened

Senior Engineering Manager specializing in cloud security and graph-based data platforms

Seattle, WA9y exp

“Engineering leader at Sysdig Secure who pitched and prototyped a model data platform that initially got rejected, then proved value by migrating the CIEM offering and expanding adoption across multiple verticals. Now owns the CIEM suite plus the broader Sysdig Secure data and reporting platforms, manages 14 direct reports, and also leads a pilot AI team while remaining hands-on weekly.”

“Senior engineering leader at AWS currently managing two services in the MSK organization, including Replicator for disaster recovery/data distribution and Glue Schema Registry. He combines hands-on distributed systems depth with large-scale people leadership, having managed teams up to 32 engineers and coordinated platform onboarding work across roughly 340 AWS services. Earlier experience in payroll and compliance platforms adds strong enterprise domain breadth beyond cloud infrastructure.”

“Engineering leader with experience at Vertex and Salesforce, combining org leadership with hands-on architecture work. He led a 40-person team through execution and quality improvements, drove legacy modernization and SaaS transition, and launched a 0-to-1 voice capability in CRM while also applying AI to code migration and developer productivity.”

Cathy Shu

Screened

Entry-level Full-Stack Engineer specializing in AI and healthcare applications

Mountain View, CA1y exp

“Early-career full-stack/AI engineer who has already owned a production RAG system for a mental-health-focused startup serving veterans and first responders. Stands out for combining LLM application design, AWS infrastructure work, and product thinking around trust, latency, and guardrails, plus building a separate agentic commerce support prototype and a stock backtesting platform.”

“CTO-minded builder seeking to join a startup; previously created an AI-driven platform that abstracted away DevOps and infrastructure for drug discovery researchers. Emphasizes high-leverage, zero-to-one execution with managed cloud/open-source tooling, and a strong reliability/reproducibility mindset validated against existing scientific pipelines.”

michael rosenbloom

Screened

Executive technology services leader specializing in cloud, managed services, and outsourcing

Tel Aviv, Israel29y exp

“Operator with founder/COO/CEO experience in professional services, managed services, and cloud services (Adjoined Consulting, Virtustream, Lemongrass). Known for instrumenting businesses with real-time pipeline/backlog-to-resource planning systems and for building sales organizations end-to-end, including bookings-based comp plans that drove ~40% CAGR. As VC-backed non-founder CEO, scaled Lemongrass from ~$25M to ~$100M run-rate and applied AI to de-risk SOW/costing and to improve service desk efficiency and gross margin.”

Krishna Reddy

Screened

Mid-level AI/ML Engineer specializing in fraud detection and clinical LLM assistants

New York, NY6y exp

“Built and deployed a production clinical support LLM assistant at Mayo Clinic using a LangChain-orchestrated RAG architecture (Llama 2/PaLM) over de-identified clinical records, integrating BigQuery with Pinecone for semantic retrieval. Focused on healthcare-critical reliability by reducing hallucinations through grounding, implementing HIPAA-aligned privacy controls (Cloud DLP, VPC Service Controls), and running structured evaluations with clinician feedback.”

“Technology-first venture builder with strong familiarity in the VC/accelerator landscape, specializing in greenfield innovation, M&A, and large-scale transformation/modernization. Described building a venture-funded retail banking greenfield startup to integrate lending-as-a-service for SME lending while meeting federal and local financial services compliance requirements.”

“Machine learning/AI engineer focused on agentic product experiences, including a parts-finding assistant and other AI-driven tools. Has worked on reinforcement learning projects, agent state management, and making AI understandable for non-technical users through visuals and simplified explanations.”

“Built and deployed a production RAG-based semantic search and summarization system for large legal/technical document sets, owning the full backend (embeddings, vector store, chunking, prompting) and driving a reported 40–60% reduction in manual review time. Experienced with LangChain/LlamaIndex plus Airflow/Temporal-style orchestration, and applies rigorous evaluation/monitoring (A/B tests, drift detection, staged rollouts) to keep agentic systems reliable. Also partnered with a supply-chain manager at TE Connectivity to deliver an AI inventory recommendation tool projected to drive millions in value.”