Experience

Developing and operating cloud-scale distributed systems and data plane features for Azure NetApp Files (ANF), with unified ownership across microservices, automated validation, and CI/CD.

• Own the design, implementation, and scalability of data plane features and RESTful APIs for Azure NetApp Files (ANF), delivering high-performance, low-latency cloud storage natively integrated in Microsoft Azure.
• Architect and scale automated validation frameworks, tooling, and CI/CD pipelines in Python to drive continuous integration, automated testing, and operational readiness.
• Deploy and operate containerized microservices on Kubernetes, incorporating observability, telemetry, and automated root cause analysis to maintain high availability and reliability for enterprise workloads.
• Collaborate across cross-functional teams spanning storage, networking, and cloud infrastructure while adopting AI-native engineering practices throughout the development lifecycle.

Built Python microservices on GKE for agentic workflows, ETL orchestration, API integrations, and financial data processing.

• Built Python microservices on GKE for agentic workflows, ETL orchestration, API integrations, and financial data processing.
• Developed a financial insights software using RAG and Gemini to extract structured insights from raw financial news content.

Architected fault-tolerant data systems and AI-powered tooling to support faculty research at scale.

• Architected fault-tolerant distributed ingestion systems processing 10TB+ of raw data and improving overall pipeline performance by ~70%.
• Built robust ingestion and web scraping infrastructure to collect millions of structured and unstructured financial records.
• Developed AI agents using RAG, vector databases, and OpenAI APIs to automate insight extraction from unstructured datasets.

Shipped full-stack features and data tooling across mission-critical applications.

• Built a data pipeline to parse, predict, and generate GeoJSON outputs from active satellite feeds, reducing runtime by ~40%.
• Contributed across multiple full-stack apps end-to-end — improving performance, scalability, and security.
• Identified and resolved critical issues in production deployments, improving stability and reliability for clients.

Research in machine learning and systems security focused on threat detection.

• Worked on threat detection and SIEM rulesets for Windows, generating datasets via SOCBED and AMIDES environments.
• Evaluated research-grade systems for ML-driven threat detection, applying security principles end-to-end.
• Replicated multiple papers via reverse engineering, demonstrating limitations of generic SIGMA rulesets.

First engineer of the Karpuragaurai Development Wing — owned core platform development and the initial technology roadmap.

• Architected and shipped scalable cryptocurrency forensics and disk forensics applications from a blank slate.
• Built UI and REST APIs in C++ kernels and Python, improving software performance by ~70% via distributed processing.
• Developed AI-driven forensic agents to automate evidence analysis, anomaly detection, and investigation workflows.
• Led a product development team to optimize algorithms and architecture for diverse client deployments.
• Streamlined deployment and testing automation by implementing DevOps, optimizing cloud and on-prem container infrastructure.

Hands-on training internship deploying end-to-end solutions on AWS.

• Completed a structured training program building production-grade solutions on AWS Cloud.
• Deployed services using CloudFront, Amplify, IAM, WAF, EC2, CI/CD, Lambda, S3, RDS, and DynamoDB.