10+ years architecting scalable AI/ML systems, cloud data platforms, and DevOps pipelines.
I am a Cloud Data, Platform, and AI Engineer with over 10+ years of experience architecting and deploying highly scalable, secure data pipelines and cloud infrastructures. My technical foundation is built on optimizing enterprise environments across AWS and Azure, utilizing Infrastructure as Code (IaC) tools like Terraform alongside containerization technologies like Kubernetes and Docker to ensure robust security. I specialize in engineering automated, multi-tenant analytics platforms and orchestrating high-performance PySpark data workflows that significantly reduce resource provisioning times and accelerate data availability. By establishing rigorous CI/CD practices and automating infrastructure provisioning, I consistently streamline deployment cycles, minimize manual integration errors, and enhance overall code quality for reliable software releases.
In addition to core platform engineering, my recent work heavily focuses on integrating advanced analytics and Generative AI into modern architectures. I have designed end-to-end Retrieval-Augmented Generation (RAG) systems using AWS Bedrock and vector databases, directly empowering data science teams to generate more accurate business insights and forecasts. My portfolio also includes automating complex OCR text extraction pipelines with AWS Textract, managing real-time BI deployments via AWS QuickSight, and constructing highly scalable, event-driven data workflows for large-scale applications. Ultimately, my focus lies in bridging the gap between complex cloud infrastructure and actionable intelligence, building resilient systems that drive data-driven decision-making while actively mentoring engineering teams on pipeline best practices.
B.Tech — Electronics & Communication Engineering
Jawaharlal Nehru Technological University, India · 2014
Designed and implemented an end-to-end Retrieval-Augmented Generation system leveraging AWS Bedrock, using Aurora PostgreSQL as a vector database and S3 as the primary data source. Automated Bedrock model provisioning via Terraform and Self-Service Portals with IAM policies in a scalable multi-tenant architecture.
Built a multi-tenant JupyterHub platform using the Zero to JupyterHub framework, creating custom Docker images for seamless Python execution in isolated single-user environments. Orchestrated deployment and scaling with Helm charts and Terraform for consistent, secure environments.
Configured AWS QuickSight for real-time analytics integrating Athena, Glue Data Catalog, and S3 with cross-account access. Automated setup with Terraform and integrated ServiceNow workflows for future dataset and user provisioning.
Open to senior engineering roles, consulting engagements, and interesting AI/cloud architecture conversations.