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  • CVS Health
  • dallas texas
  • Joined May 10, 2026

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devavipul15/README.md

Hi πŸ‘‹, I'm Vipul Deva

Senior Generative AI / AI/ML Engineer β€’ Agentic AI β€’ Enterprise LLM Platforms


πŸš€ About Me

I’m a Senior Generative AI / AI/ML Engineer specializing in enterprise-scale LLM platforms, Agentic AI systems, Retrieval-Augmented Generation (RAG), GraphRAG, and cloud-native AI infrastructure.

I design scalable AI ecosystems combining multi-agent orchestration, distributed systems, observability, intelligent automation, and production-grade deployment strategies across healthcare, banking, retail, and enterprise domains.

  • πŸ”­ Building enterprise-scale Agentic AI platforms
  • 🧠 Designing scalable RAG and GraphRAG architectures
  • ⚑ Developing production-grade LLM systems and AI infrastructure
  • ☁️ Experienced across GCP, AWS, and Azure ecosystems
  • πŸ—οΈ Architecting distributed AI pipelines and event-driven systems
  • πŸš€ Passionate about AI reliability, observability, and intelligent automation

πŸ› οΈ Core Tech Stack

Languages & Frameworks


πŸ€– Generative AI & LLM Engineering


☁️ Cloud & Infrastructure


πŸ“Š Data, Vector Search & Observability


πŸš€ Enterprise Expertise

βœ”οΈ Agentic AI Systems
βœ”οΈ Multi-Agent Workflows
βœ”οΈ Retrieval-Augmented Generation (RAG)
βœ”οΈ GraphRAG Architectures
βœ”οΈ LLM Orchestration
βœ”οΈ AI Gateway Architectures
βœ”οΈ AI Reliability & Observability
βœ”οΈ Distributed AI Systems
βœ”οΈ Real-Time AI Pipelines
βœ”οΈ Cloud-Native AI Infrastructure
βœ”οΈ Semantic Search & Vector Databases
βœ”οΈ Production AI Platforms


πŸ“ˆ Impact Highlights

  • πŸš€ Improved clinical decision efficiency by 40% using enterprise Agentic AI workflows
  • ⚑ Enhanced fraud detection accuracy by 25% through real-time AI/ML pipelines
  • πŸ“Š Improved forecasting accuracy by 20% using scalable distributed analytics systems
  • 🧠 Built enterprise-grade RAG and LLM platforms supporting intelligent automation
  • ☁️ Designed scalable cloud-native AI ecosystems using GCP, AWS, and Azure

πŸ—οΈ Current Focus Areas

  • Enterprise AI Platforms
  • Multi-Agent AI Systems
  • GraphRAG Architectures
  • LLMOps & Observability
  • AI Reliability Engineering
  • Intelligent Workflow Automation
  • Responsible AI & Governance
  • Distributed AI Infrastructure

🧩 Architecture Interests

  • Event-Driven AI Systems
  • AI Gateway Architectures
  • Multi-Model Routing
  • Distributed AI Pipelines
  • Scalable Vector Search
  • Enterprise RAG Infrastructure
  • Agent Planning & Memory Systems
  • Production AI Reliability Patterns

🏒 Domains

Healthcare β€’ Banking β€’ Retail β€’ Government β€’ Enterprise Analytics


⚑ Philosophy

Building scalable, reliable, and production-grade AI systems powered by LLMs, Agentic AI, distributed architectures, and intelligent automation for real-world enterprise impact.


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