Vetted Google Cloud Platform Professionals

Shreya Sivakumar

Screened

Mid-level Machine Learning & Generative AI Engineer specializing in AI agents and LLM workflows

4y exp

“Customer-facing AppSec/solutions engineer with experience securing cloud-native AI/LLM deployments on Azure and Kubernetes. Led threat modeling and production hardening (Key Vault secrets migration, least-privilege IAM, rate limiting, structured logging/monitoring, LLM guardrails) and has supported retail search/catalog platforms using Elasticsearch, including performance triage and rollout playbooks that improved customer trust and enabled engagement expansion.”

Sviatoslav Boichuk

Screened

Executive CTO / Engineering Leader specializing in Full-Stack Architecture and Cloud Delivery

Ivano-Frankivsk region, Ukraine11y exp

“Founder building a hiring-focused startup who has engaged with venture investors but is prioritizing direct end-user traction through email marketing and other outreach before returning to raise a seed round. Has experience working with startups that have already raised seed funding and demonstrates a structured approach to market validation (customer conversations, landing pages) prior to heavy development.”

Vishnu Priyan Sellam Shanmugavel

Screened

Mid-Level Applied AI Engineer specializing in LLM services, RAG, and OCR/NLP extraction

Arlington, VA4y exp

“Backend/platform engineer who built and evolved a large-scale healthcare document processing system (OCR + LLM orchestration) in Python/FastAPI on Google Cloud (Cloud Run, GCS, Firestore), processing ~1.5M files per batch and tens of millions overall. Emphasizes reliability and operational safety via deterministic IDs, idempotent state machines, strong observability, and self-healing reconciliation, plus disciplined migrations using dual-run validation and incremental rollouts.”

“Marketing and product-focused analytics candidate with hands-on experience turning messy large-scale data from Hadoop/HDFS, Azure Data Lake, and transaction systems into validated reporting tables. They combine SQL and Python automation with strong metric design, cohort/retention analysis, and stakeholder-friendly dashboards, including a reported 30% query performance improvement and weekly reporting automation.”

“Full-stack developer with seven years of experience who has built production systems across AWS serverless infrastructure, Django applications, and user-facing web products in domains like emergency response, fintech/investing, recruiting, and conference management. Particularly notable for combining technical architecture with product thinking—e.g., modernizing a crash-tracking platform for emergency responders and materially improving trust-driven conversion in a trading-card fractionalization product.”

“Backend/AI engineer with deep healthcare experience building production Python microservices that turn raw clinical audio into structured notes and insights. They owned systems end-to-end across architecture, launch, monitoring, and incident response, with measurable impact including 40% lower operating costs, 22% better latency, and 99.9% uptime in a regulated environment.”

“Built and owned a production RAG-based internal knowledge assistant end-to-end, from experimentation through cloud deployment and monitoring. Demonstrated strong practical GenAI judgment by choosing prompt optimization and retrieval tuning over fine-tuning for dynamic data, driving a 40% to 50% reduction in time to answer while improving relevance, lowering hallucinations, and increasing productivity.”

“Built an end-to-end agentic population health strategy copilot for healthcare leadership, turning broad chronic disease questions into structured, evidence-backed strategy briefs. Stands out for combining healthcare domain knowledge with production-grade GenAI implementation, including LangGraph orchestration, Databricks/MLflow deployment, human review, and quality gates focused on citations, metrics, risks, and safety.”

Narayan Anantha Krishnan

Screened

Junior AI/ML Engineer specializing in LLMs, RAG, and cybersecurity

Syracuse, NY2y exp

“AI/full-stack builder with hands-on experience shipping conversational and agentic products, including a travel itinerary assistant, a multi-agent data analysis platform, and a self-correcting RAG system. Also brings academic research depth from Syracuse University, where they helped develop tiny-LLM-based IoT threat mitigation and presented an accepted paper at FLAIRS 39.”

Antonio Walker

Screened

Senior Technical Lead and Full-Stack Engineer specializing in cloud, AI, and enterprise platforms

Irvine, CA13y exp

“Engineering leader and player-coach who says he joined Freeing Returns during a transition from sales-led services to SaaS, architected the platform from the ground up, and helped hire a 10+ person team across engineering, product, and delivery. He also describes leading an AI-based fraud detection system on Salesforce with data lake and pipeline architecture, combining startup build-from-scratch execution with hands-on technical leadership.”

SIVA RAMAKRISHNA PALADI

Screened

Mid-level AI/Full-Stack Engineer specializing in agentic AI and RAG systems

Santa Clara, CA5y exp

“Solo builder who shipped two ambitious AI products from scratch: Zoly, a healthcare/pharmacy automation platform with voice agents, RAG, clinician dashboard, and patient app live in 4 months, and Breeth, a contextual memory system for AI agents deployed on AWS. Particularly compelling for teams needing a hands-on full-stack/AI engineer who can operate in ambiguity, design for safety and compliance, and turn complex agent workflows into production products.”

“Software engineering intern who built and deployed a full-stack telemedicine platform (React/Node/MongoDB) used daily in a pediatric clinic, incorporating PyTorch-based predictive features. Demonstrated strong customer-facing iteration and production performance debugging—resolved a live slowdown by indexing/optimizing MongoDB queries and adding caching, improving response times by ~50%.”

“Backend/distributed-systems engineer with 9 years of Go experience, focused on financial-services platforms where performance, reliability, and regulatory auditability are critical. He has built low-latency market data infrastructure (p99 under 8ms) and optimized compliance/reporting systems used by finance and audit teams, combining strong systems design with practical production operations.”

Mila Chernik

Screened

Executive strategy and operations leader specializing in SaaS, payments, and AI

New York, NY17y exp

“Operator and transformation leader with deep SaaS/payments experience across fintech, ecommerce, retail, and hospitality. Has led large cross-functional organizations through Agile, portfolio, and architecture transformations, including $20M ARR impact at Movex and major performance/cost gains at HS-Soft. Also brings founder-level strategic advisory experience, building ROI cases for growth and AI automation investments.”

“Built a production multi-agent RAG assistant using LangChain/LangGraph with OpenAI embeddings and FAISS, focusing on retrieval quality and latency (Redis caching, parallel retrieval, precomputed embeddings). Experienced orchestrating ETL/ML pipelines with Airflow and Databricks Workflows, and has delivered an AI assistant for business ops to extract insights from policy/compliance documents through close non-technical stakeholder collaboration.”

“Startup engineer who owned an end-to-end carpool marketplace experience at FavorIt (React Native, Firebase/Firestore, Cloud Functions, Stripe) and iterated rapidly using Mixpanel + feature flags while applying rigorous integrity controls for booking and payments. Also built a TypeScript/React + Go/Postgres workout tracker and previously worked on Spring Boot microservices for financial-institution workflow automation with event-driven patterns (outbox, idempotency, backpressure tuning).”

“Built and deployed production LLM applications including a natural-language-to-read-only-SQL system focused on ambiguity handling and query safety (schema whitelisting, intent validation, confidence checks, deterministic execution). Experienced with LangChain-based, modular agent orchestration and RAG document QA for large PDFs, with a metrics-driven testing/evaluation approach and cross-functional delivery with marketing on an AI content recommendation/search tool.”

“Built and deployed a production AI-powered personalized learning platform (Django + FastAPI) featuring an LLM+RAG tutoring assistant and automated grading. Demonstrates strong applied LLM reliability engineering (structured JSON outputs with Pydantic validation, hallucination control via FAISS-based RAG thresholds and refusals) plus scalable async microservice design and Airflow-orchestrated ETL across AWS/GCP.”

“Backend/DevOps-leaning engineer who has owned a Python serverless platform on AWS (Lambda, DynamoDB, Step Functions), including complex multi-step business workflows with transaction-based consistency and robust failure handling. Also supported an on-prem SQL to Azure Data Lake migration by building and monitoring Python + Azure Data Factory ETL pipelines, and led GitOps-style CI/CD automation with GitHub Actions (tests, security scans, automated deployments).”

“Co-op engineer who owned and shipped a Python/Flask backend for automating architecture reviews and system metadata processing, including ingestion from multiple internal APIs, RBAC, testing, and deployment. Has hands-on Kubernetes + GitOps (ArgoCD) experience, built Kafka-based real-time ingestion, and supported a cloud-to-on-prem migration with phased cutover, smoke tests, and performance tuning.”

“Frontend-leaning engineer who shipped an end-to-end map-based discovery feature in a React Native mobile app, integrating location-based REST APIs with strong UX polish (loading/empty/error states) and cross-platform performance fixes. Also has experience building a Python backend with JWT auth and layered service structure, plus prior infrastructure work setting up centralized logging and monitoring.”

Ahmad Alomari

Screened

Senior Data Scientist & Machine Learning Engineer specializing in computer vision and production ML

Cleveland, OH7y exp

“PhD in computer engineering who has built production-oriented ML/NLP systems for space-weather prediction using Spark-based ETL on noisy satellite sensor logs. Strong in entity resolution and semantic search—fine-tuned E5 embeddings with contrastive learning and deployed to Pinecone, improving top-5 retrieval precision by 25%—and emphasizes scalable, observable pipelines with Airflow, Docker, and CI/CD.”

Ramya Jonnala

Screened

Principal/GenAI Engineer specializing in LLMs, RAG, and MLOps

Plano, TX9y exp

“Built a production AI-powered university marking system that automates question generation and grading from PDF course materials using a RAG pipeline (S3 + Pinecone) orchestrated with LangChain/LangGraph and deployed on AWS ECS via Docker/ECR and GitHub Actions CI/CD. Addressed a key real-world LLM challenge—grading consistency—by implementing rubric-based scoring, retrieval re-ranking, and standardized context summarization, validated against human instructors.”