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Vetted Google Cloud Platform Professionals
Pre-screened and vetted.
Abhirup Chakraborty
Junior Software Engineer specializing in backend systems, AI, and search
“Built a complex graph-based search engine to find connections between people and has hands-on experience designing multi-agent coding pipelines that move features through implementation, test generation, testing, and sanity checks. Stands out for treating AI agents like an engineering team, with shared-memory coordination, queue signaling, and completeness-focused guardrails to improve reliability and reduce ambiguity.”
Santhosh padamatinti
Senior Engineering Manager specializing in data platforms, microservices, and enterprise GTM analytics
“Engineering leader (player-coach) recently at Autodesk driving a major sales-motion transformation spanning account hierarchy, commissions/quotas, and downstream financial/sales forecasting impacts. Led cross-functional design with enterprise architects and shipped the end-to-end release, using POCs and Anaplan what-if modeling to validate a risky hierarchy change while coordinating delivery across India/Singapore teams and instituting structured Jira-based tech debt/support tracking and automation (dbt, GitHub Copilot).”
Moh Abdullah
Senior AI/ML Engineer specializing in Generative AI, LLMs, and production ML systems
“ML/AI engineer with hands-on ownership of both classical ML and GenAI systems in production. They built an end-to-end churn prediction service on AWS and also shipped RAG-based document search/summarization features, with clear experience in monitoring, hallucination reduction, cost/latency optimization, and creating shared Python/LLM infrastructure used across teams.”
Rajeshwar Ragupatula
Director-level AI Architect/Manager specializing in GenAI, MLOps, and enterprise automation
“GenAI/ML engineering leader (player-coach) who built and deployed an image-to-text production system for topology/resource diagrams, combining YOLO-based issue detection with an LLM to generate support-ready reports at scale. Heavy AWS stack (SageMaker, Step Functions, Lambda, CloudWatch, FastAPI, Kubernetes/Docker) with KPI-driven optimization (MTTR, P50), including ~21 custom labels and reported 30–50% faster issue identification while processing thousands of images in production.”
Mid-level MLOps/DevOps Engineer specializing in cloud automation and ML pipelines
Mid-level Full-Stack Java Developer specializing in microservices and cloud-native web apps
Staff Cloud Automation Engineer specializing in cloud infrastructure automation and SRE
Mid-level AI Engineer specializing in Generative AI, LLMs, and RAG systems
Mid-level Cloud & DevOps Engineer specializing in cloud automation and Kubernetes security
Junior Software Engineer specializing in Python full-stack, cloud/DevOps, and AI/ML
Mid-level Python Full-Stack Developer specializing in AI-driven cloud applications
Staff Software Engineer specializing in Healthcare Platforms and Observability
Mid-Level Software Engineer specializing in cloud platforms and agentic AI automation
Staff Software Engineer specializing in cloud-native healthcare platforms and ML systems
Junior AI Engineer specializing in agentic systems, LLM evaluation, and RAG
Senior Data Engineer specializing in AI/ML platforms and legal data pipelines
Software Engineering Intern specializing in full-stack systems and AI-driven products
Mid-level AI Engineer specializing in Ambient AI and full-stack applications
Entry-Level Software Engineer specializing in full-stack web and platform engineering
Executive Technology Leader specializing in AI, FinTech, and Blockchain