Vetted Google Cloud Platform Professionals

Vamshikrishna Bandi

Screened

Senior AI/ML Engineer specializing in Generative AI and agentic multi-agent systems

6y exp

“Built and shipped a production LLM-powered multi-agent RAG system to automate complex internal support workflows, integrating tool execution (SQL/APIs) with validation guardrails to reduce hallucinations. Optimized for real-world latency and cost via model routing, caching, and async parallel tool calls, and enforced reliability with CI-gated golden test sets derived from anonymized production queries.”

Lamar Petty

Screened

Mid-level Full-Stack Product Engineer specializing in data-driven web apps and healthcare systems

San Francisco, CA13y exp

“Full-stack engineer with production experience shipping a healthcare-focused web app (Pregnancy-Pal) using Next.js/TypeScript on GCP, integrating a Python/Flask middleware and FHIR server for patient/practitioner dashboards and messaging. Former Wikimedia Foundation Android engineer who led the end-to-end 'Year in Review' feature and built robust automated testing/CI practices (Espresso, GitHub Actions matrix). Strong emphasis on reliability via rigorous validation, comprehensive Postman testing, and detailed API documentation.”

Praveen Nutulapati

Screened

Mid-level Generative AI Engineer specializing in LLM fine-tuning, RAG, and agentic systems

New York, NY6y exp

“Built and deployed a production multi-agent RAG system at JPMorgan Chase to automate regulated credit analysis and compliance clause discovery across large internal policy/document libraries. Implemented LangGraph-based supervisor orchestration with structured state management (Azure OpenAI) to support long-running, resumable workflows, plus hybrid retrieval + re-ranking and guardrails for reliability. Strong at evaluation/observability (trace logging, LLM-judge, HITL) and at communicating results to non-technical stakeholders via Power BI embeds and Streamlit prototypes.”

Sunil Parikh

Screened

Executive enterprise architect specializing in cloud, cybersecurity, and platform modernization

Plano, TX26y exp

“Architect with early startup experience (1999-2000) who later worked with Capital One evaluating startup products, strategy, and roadmaps. Brings a structured approach to innovation through market research, competitor analysis, risk assessment, gap analysis, and proof-of-concept thinking.”

Ishwari Mulay

Screened

Intern Data Scientist specializing in analytics and healthcare data

Philadelphia, PA1y exp

“Analytics candidate with AstraZeneca internship experience building scalable SQL and Python workflows on large healthcare datasets. Stands out for combining data engineering, reporting automation, and applied machine learning— including an end-to-end patient no-show prediction project that achieved 76.8% accuracy and reduced no-shows by 18%.”

“Built Baioniq, an enterprise LLM platform for automating extraction from massive unstructured documents like contracts and insurance claims. They demonstrate unusually strong production depth in agentic AI—scaling to 100k+ requests/day, processing 1M+ claim documents, and improving extraction accuracy through rigorous RAG architecture, evaluation, and fallback design.”

Sirisha Maddikunta

Screened

Mid-level Generative AI Engineer specializing in enterprise LLM and healthcare AI solutions

O Fallon, MO6y exp

“Built and owned an end-to-end LLM-powered fraud investigation assistant that automated case summaries and risk analysis, cutting analyst investigation/documentation time by 40%. Stands out for translating RAG concepts into a production-grade internal platform with strong evaluation, monitoring, and reusable Python service architecture that improved both analyst trust and engineering velocity.”

“Candidate brings a blend of consulting and brand-side marketing sourcing experience from Deloitte Consulting and L’Oreal. They stand out for using deep research, cost analysis, and one-on-one stakeholder engagement to drive agency negotiations, support a $4M annual savings target, and win adoption of new project tools like Smartsheet in change-resistant client environments.”

“Built and owned a production conversational AI platform for a healthcare contact center, including RAG-based agent assist, hybrid retrieval, safety guardrails, and production monitoring. Stands out for combining LLM product delivery with strong operational rigor, driving a reported 25-30% improvement in handling time in a sensitive healthcare environment.”

“ML engineer with production experience at PayPal and Flipkart, owning high-scale systems across fraud detection, recommendations, and LLM tooling. Stands out for combining strong modeling judgment with practical platform engineering, delivering measurable impact like 22% fewer fraud false positives, 18% CTR lift, 40% less LLM manual review, and 30% faster redeployments.”

“Backend engineer with production experience on an AWS Lambda-based payment service (manually deployed) and hands-on modernization work using parallel-run + diffing to prove parity before cutover. Has practical production troubleshooting experience for batch/pipeline incidents using monitoring/logs and emphasizes idempotent rerunnable jobs for safe recovery; also improved GraphQL performance by refactoring overly broad queries.”

“Software engineer focused on reliability and scalable systems: built React/TypeScript dashboards backed by Java/Spring Boot APIs and designed Kafka-based microservices with strong contract/versioning discipline. Known for shipping incremental improvements with tight feedback loops and for creating internal observability tools that streamline on-call and incident diagnosis under high-traffic conditions.”

“LLM/agentic systems builder at Verizon who deployed a LangGraph-orchestrated multi-agent ticket-automation platform with RAG (FAISS) to replace brittle rule-based bots. Improved routing correctness by ~30–40%, hit ~300ms latency targets via model routing, and reduced ops workload by ~60% through tight iteration with non-technical stakeholders and strong testing/observability practices.”

“Solutions Engineer focused on application and platform security for enterprise cloud-native deployments, advising customers on threat modeling and secure CI/CD practices across AWS and Kubernetes. Has implemented SCA/container scanning and vuln checks in pipelines, tuned thresholds to reduce false positives, and driven outcomes like faster security approvals and smoother production rollouts. Troubleshot high-load Kubernetes failures (OOMKills, registry throttling) and turned fixes into a standard tuning guide.”

“Backend/platform engineer with experience across edtech, logistics, and AWS internal systems—owned a production course recommender end-to-end (model serving + APIs + caching/observability), delivering +30% CTR and -20% latency. Has scaled real-time delivery visibility/rerouting on Kubernetes/EKS to sub-200ms P95 during demand spikes and built billion-events/day telemetry pipelines on AWS (Kinesis Firehose, Lambda, S3, Redshift) with schema evolution, dedupe, and replay support.”

“Infrastructure/DevOps engineer with primary ownership in enterprise Linux and AWS/Azure production environments (including financial systems). Built secure, repeatable CI/CD pipelines deploying containerized workloads to EKS/ECS and implemented Terraform/CloudFormation IaC with drift detection and rollback practices; lacks direct IBM Power/AIX/PowerHA experience.”

Velusamy Ramasamy

Screened

Principal Software Architect specializing in cloud platforms, data engineering, and enterprise security

Seattle, WA29y exp

“Engineering leader with experience defining solutions from business requirements through detailed specifications and implementation, emphasizing cost-aware technology selection. Has led architectural changes including adding IBM Cloud alongside AWS for budget reasons and integrating caching/messaging to improve availability and performance, and describes scaling distributed teams via experienced DevOps/QA hires and structured evaluation.”

ABHIJOY SARKAR

Screened

Senior AI Engineer specializing in LLMs, agentic systems, and MLOps

San Francisco Bay Area, CA8y exp

“Built and shipped PromptGuard, a production middleware proxy that secures GenAI RAG/agent systems against prompt injection and unsafe tool use using risk scoring, graded policy actions, and least-privilege tool gating. Also replaced LangChain abstractions with a custom state-machine runner for a production voice agent to reduce latency and improve traceability, and delivered a clinic call assistant by converting front-desk/doctor requirements into scenario-based guardrails and measurable evals.”

Madhu Swetha Kommi

Screened

Intern Full-Stack Software Engineer specializing in cloud data pipelines and internal tools

1y exp

“Built an internal Meta tool (HiVA Bot) for notification customization and end-to-end task tracking around advertiser-reported issues, including chat-thread creation, org-hierarchy opt-ins, SLA reminders, and search/typeahead features. Implemented the system with a Java/Spring Boot microservices approach and asynchronous patterns, and supported adoption via internal wiki documentation.”

Shobana Chandrasekaran

Screened

Mid-Level Software Engineer specializing in AI microservices and generative fashion

Sunnyvale, CA2y exp

“Backend/AI workflow engineer at a startup building production AI services for fashion workflows, including an AI-powered techpack generation API in Go (Gin) with MongoDB handling ~1k+ daily requests. Recently implementing an image-to-3D dress generation feature end-to-end, integrating a Python FastAPI AI service with ComfyUI + Hunyuan, with strong emphasis on async orchestration, webhooks, and observability (OpenTelemetry + SigNoz).”

Shriya Bannikop

Screened

Mid-level Software Engineer specializing in cloud platforms, data engineering, and distributed systems

Seattle, WA5y exp

“Full-stack engineer who built and owned an AI-assisted job-matching dashboard in Next.js App Router/TypeScript, keeping LLM logic server-side and improving performance via deduplication, caching/revalidation, and streaming (35% fewer duplicate LLM calls; 40% faster first render). Also has strong data/backend chops: designed Postgres models and optimized queries at million-record scale (1.8s to 120ms) and built durable AWS multi-region telemetry workflows with idempotency, retries, and monitoring.”

Antara Bhavsar

Screened

Mid-level Software Engineer specializing in cloud-native systems and Android development

Bloomington, IN3y exp

“Application-focused software engineer with experience at Amazon and Motorola shipping production systems ranging from developer monitoring/on-call tooling (Alcazar, ~40% MTTR improvement) to consumer AI features used by 100K+ users. Currently building an AI/ML-driven platform with a Python/FastAPI backend on AWS (ECS/RDS/S3) and has handled real production latency/scaling incidents end-to-end.”

Shyaam Sundhar Rajamadam Srinivasan

Screened

Director-level Cybersecurity Architect specializing in cloud security, threat detection and incident response

Reston, VA18y exp

“Entrepreneur building an AI-enabled platform to help tradespeople (e.g., plumbers/carpenters) find customers, currently moving from ideation toward MVP and user launch. Previously founded Votimo (2012), an early philanthropy/donation-transparency platform, raised ~ $2M in angel funding, and implemented the system before strategic drift from leadership/vision changes.”

“Data engineer with experience at Expedia building SQL Server and Azure Data Factory pipelines for business reporting and analytics. Stands out for pragmatic end-to-end pipeline ownership in ambiguous environments, with a strong emphasis on data quality, rerunnability, query performance, and making downstream datasets reliable for other teams.”