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Vetted Kubernetes Professionals
Pre-screened and vetted.
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.”
Garretson Beebe
Director-level Engineering Leader specializing in cloud, data, and AI platforms
“Engineering leader with deep product-platform experience across Splunk and Alation, including owning the multi-year SPL2 initiative at Splunk from customer research and architecture decisions through execution planning and org scaling. Also grew Alation's Data Analytics organization from 8 to 22 engineers across three squads, combining hands-on technical problem solving with strong managerial systems.”
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 Software Engineer specializing in ML platforms and full-stack systems
Mid-level AI Engineer specializing in Generative AI, LLMs, and RAG systems
Mid-Level Software Development Engineer specializing in AWS automation and release pipelines
Mid-level Cloud & DevOps Engineer specializing in cloud automation and Kubernetes security
Mid-level Full-Stack Software Engineer specializing in FinTech and cloud-native microservices
Mid-level Full-Stack Developer specializing in cloud-native microservices and FinTech
Junior Software Engineer specializing in Python full-stack, cloud/DevOps, and AI/ML
Mid-Level Full-Stack Java Developer specializing in FinTech microservices
Mid-level Full-Stack Software Engineer specializing in cloud microservices and distributed systems
Mid-level Full-Stack Software Engineer specializing in cloud and GenAI applications
Mid-level Backend Engineer specializing in cloud migrations and scalable microservices
Mid-level Python Full-Stack Developer specializing in AI-driven cloud applications
Staff Software Engineer specializing in Healthcare Platforms and Observability
Senior Linux Systems Engineer specializing in telecom, private 5G ORAN, and cloud-native platforms
Mid-Level Java Full-Stack Developer specializing in Spring Boot, Angular, and AWS