Architected and developed enterprise-grade blockchain address monitoring microservices using Java Spring Boot, enabling compliance teams to perform real-time risk assessment and streamline user onboarding for decentralized apps.

Implemented event-driven architecture with Apache Kafka to process blockchain transaction data at scale, integrated with PostgreSQL for risk scoring and compliance audit trails.

Delivered transformational business impact by establishing automated compliance workflows that enabled onboarding of 50+ decentralized applications, resulting in 300% revenue growth and positioning the platform as a trusted Web3 gateway.

Deployed cloud-native solution using Docker containerization and Kubernetes orchestration, ensuring high availability and scalability for processing millions of blockchain addresses daily.

Developed highly scalable async metadata tagging API using Go/Redis task queues, reducing latency from 8 seconds to 200ms.

Built highly available (99.5%) gRPC endpoint ingesting data from S3/GCS/Azure, achieving 5x upload acceleration, resulting in securing high-value multiple enterprise partnerships and revenue increase through improved platform performance.

Led end-to-end software development lifecycle while cross-collaborating with product, business and UI/UX

The feature had a huge impact on making the customer happy, as it made the user journey easy and pleasant, as the feature was the first point of action on our platform.

It increased the scalability of the platform by offloading the front-end, which was making numerous Post requests previously, and also enabled the extensibility of the tagging feature.

Developed a feature to ingest data from the three major cloud storage providers- AWS S3, GCP storage, and Azure blob storage into Clarifai’s data input pipeline.

Implemented it as a scalable async microservice, running on Kubernetes pods in an AWS EKS cluster using Go lang, PostgreSQL, and Redis, integrating SDKs provided for S3, GCP, and blob storage.

Collaborated with cross-functional teams to gather user stories, design requirements, and implementation details. Co-authored the design doc with the team lead.

This is a feature that delighted customers and is the most awaited functionality since it eases the uploading process of high volumes of large datasets with media files at scale onto the Clarifai platform.

Architected and developed high-performance REST APIs for anomaly detection and capacity explorer services using Python Flask, implementing caching strategies, pagination, and asynchronous processing to optimize API response times and handle concurrent user loads.

Led critical backend development during enterprise SaaS migration from on-premises infrastructure to AWS cloud, leveraging EC2, RDS (PostgreSQL), S3, Lambda, Kafka, and Snowflake to achieve enhanced scalability, 99.9% availability, and reduced operational costs.

Optimized API performance through database query optimization, connection pooling, and AWS ECS containerization, enabling the platform to scale from single-tenant to multi-tenant SaaS architecture supporting hundreds of concurrent users.

Implemented cloud-native data pipeline integrating Snowflake data warehouse with real-time Kafka streaming and S3 storage, ensuring seamless data flow between analytics services and improved system reliability

Developed a cluster monitoring, alerting and logs collection service that gets critical cluster information across several multi-node clusters in a data center. This project improved the efficiency of resource utilization, management by 75% and saved $250k on resource spending.