Grading: Evaluated full-stack web development projects focused on client-server architecture, version control, testing, and front-end design. Technologies included JavaScript, Node.js, Express, React, Next.js, Tailwind CSS, Chai.js, and Git.

Automation & Scripting: Developed shell/BASH scripts to automate grading tasks by cloning student Git repositories, counting commits, installing dependencies via npm, and running custom test cases significantly reducing manual effort and improving grading consistency.

Scalable Recommendation System (500k RPM): As a core member of the Personalization Team, engineered a distributed recommendation engine handling traffic peaks of 500,000 requests per minute. Maintained 99.99% availability during blockbuster releases by implementing Circuit Breakers and custom connection pooling.

High-Scale Architecture: Led the strategic migration of the legacy monolith to a high-concurrency Go (Golang) microservice architecture. This reduced AWS infrastructure costs by 40% while serving millions of users across Movies, Events, and TVOD.

Performance Engineering: Designed a Scatter-Gather concurrency pattern using Go Routines to aggregate various user data and content data in parallel. This slashed p99 latency by 50% (300ms to150ms), directly improving the user experience on the Home Page and Top Nav.

Search Infrastructure: Engineered the "Trending Searches" data pipeline, ingesting real-time user query logs to dynamically update the Search API. This feature drove content discovery and increased user engagement during peak booking windows.

Cloud Migration (AWS): Executed the on-premise to AWS cloud migration for the Personalization tech stack. Successfully migrated Terabytes of user data with zero data loss, ensuring high availability for downstream applications.

Real-Time Data Engineering: Engineered high-throughput ETL pipelines using Apache Spark (Scala) and Kafka. Integrated clickstream data from Redshift/SQL into Elasticsearch and Aerospike to power real-time personalization algorithms.

Workflow Automation: Developed automated data ingestion workflows using Apache NiFi, streamlining the transfer and transformation of data between Kafka streams and transactional SQL databases.

Observability & Monitoring: Implemented comprehensive system observability using Elastic APM and Coralogix. Configured critical Slack/Email alerts for API latency and error rates, significantly reducing Mean Time to Detect (MTTD) for production incidents.

System Stability: Stabilized and maintained critical legacy Java/Scala applications, ensuring 100% uptime during the high-traffic post-pandemic platform resurgence.

Market Research Automation: Built a Python-based web scraper to aggregate and index biotech industry news and employer contact details, replacing manual data collection with automated Excel report generation.

Technical Implementation: Utilized BeautifulSoup and Requests to extract data from multiple external sources, implementing robust error handling and pagination logic to ensure reliable data ingestion