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Venkata Subba Raju Nadimpalli

@venkatasubbarajunadi

Senior AI Engineer at Avesha Systems building LLM agent orchestration, Kubernetes cost attribution, and autonomous scaling systems.

United States

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At Avesha Systems, I designed a multi-tenant FinOps cost attribution platform for Kubernetes environments, with namespace- and node-level cost splitting across AWS, OCI, and Linode. I also built an agentic orchestration layer that coordinates LLM-powered agents through MCP servers and tool-augmented workflows.

I architected an AI-powered capacity estimation engine to model RPS-resource relationships and SLA adherence, and led development of an agentic scale-to-zero system for multi-tenant Kubernetes clusters.

At Sway AI, I contributed to 10+ platform features, integrating machine learning models and helping bring LLM capabilities and agentic workflow demonstrations to the product. I also worked on its Kubernetes migration, which improved scalability by 60% and delivered 99.9% uptime reliability.

Earlier, at Signify R&D, I deployed a YOLOV5 computer vision model on AWS with higher precision and performance than the previous benchmark. At Schneider Electric, I developed image-recognition models and integrated a product classifier into an Android application.

Experience

Work history, roles, and key accomplishments

Current

Senior AI Engineer

Current

Avesha Systems

Sep 2025 - Present (1 year 1 month)

Designed and developed a multi-tenant FinOps cost attribution platform with namespace- and node-level cost splitting. Built an agentic orchestration layer coordinating LLM-powered agents via MCP and tool-augmented workflows, and architected an AI-powered capacity estimation engine for self-adaptive autoscaling.

AWS OCI Linode Kubernetes MCP LLM FinOps Cost Attribution Agent orchestration Statistical Inference Machine Learning AutoScaling

Senior Machine Learning Engineer

Sway AI

Aug 2022 - Aug 2025 (3 years)

Developed 10+ critical features for the platform, integrating ML models (H2O, XGBoost) and LLMs into a no-code experience. Migrated the product to Kubernetes, improving scalability by 60% and achieving 99.9% uptime, and led multiple end-to-end product initiatives while mentoring 4 junior engineers.

ML/AI Research Intern

Signify R&D

May 2022 - Aug 2022 (3 months)

Researched and developed computer vision frameworks to detect small objects in video. Deployed a custom YOLOV5 model on AWS with 70% higher precision and 2x performance, and applied DeepSORT and StrongSORT tracking algorithms.

Computer Vision YOLO V5 AWS Object Tracking Deep SORT StrongSort

Machine Learning Intern

Schneider Electric

May 2020 - Aug 2020 (3 months)

Enhanced product image detection by fine-tuning a custom ML model with 96% accuracy using GANs and CNN. Established a product classifier using Tesseract and PyOCR with MobileNet V2, integrated into an Android application.

Machine Learning Gan CNN Tesseract PyOCR MobileNetV2 Android