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Rajeshwar Ragupatula
Director-level AI Architect/Manager specializing in GenAI, MLOps, and enterprise automation
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About
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.
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Key Strengths
- Shipped production GenAI image-to-text system combining YOLO object detection + LLM report generation; processing thousands of images in prod
- Delivered measurable ops impact: reduced issue identification time ~30–50% and improved MTTR via ~21 custom labels
- Deep AWS-based MLOps/pipeline ownership (SageMaker, Step Functions, Lambda, CloudWatch, FastAPI, Kubernetes/Docker) with real-time scalability focus
- Player-coach leadership: ~60% project/program management while staying hands-on with requirements, design docs, architecture, POCs, testing, and go-live/hypercare
- Strong stakeholder alignment through shadowing end users (NOC) and using POCs to validate value against business outcomes
- Improved delivery speed/quality via modular multi-agent architecture and governance-as-code practices enabling parallel development
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