A selection of work in Cloud Architecture, Automation, and Avionics.
Role: Software Developer @ Keypixel IT Solutions
Orchestrated modular VPC architectures using Terraform and AWS CloudFormation. Achieved 100% configuration parity between development and production environments, mirroring complex hardware testing labs.
Role: Software Developer @ Keypixel IT Solutions
Engineered a high-throughput data ingestion pipeline using AWS Lambda and DynamoDB. Utilized Boto3 for automated data validation, reducing manual analysis time by 40%.
Role: System Engineer @ TCS
Performed Hardware-Software Integration (HSI) testing for the Trent XWB engine. Validated control system behavior in simulated aerospace environments ensuring compliance with DO-178B standards.
Role: AWS Developer @ Arizona State University
Automated complex business logic by integrating AWS Step Functions with Lambda, improving process efficiency by 25%. Optimized Amazon RDS queries and reduced incident response time by 30% through proactive CloudWatch monitoring.
Role: Personal Project
Built an end-to-end Python pipeline that ingests messy CSV measurement data, performs automated cleaning (null/duplicate handling and normalization), applies 3-sigma Statistical Process Control rules to detect excursions, generates trend/control-limit charts, and exports a formatted PDF report. Added SQLite run logging for traceability and repeatable analysis from a single command.
Role: Final Year B.Tech Project (with ML Upgrade)
Developed a Raspberry Pi-based hardware-software pipeline that fuses IR, load cell, and moisture sensor inputs, classifies waste into four categories using a Random Forest model, and actuates a servo motor for physical bin sorting. Implemented SQLite logging for every classification event to track accuracy, analyze misclassifications, and support iterative model retraining.
Role: Personal Project
Built a yield intelligence workflow on the real UCI SECOM semiconductor dataset (1,567 wafer runs, 590 process parameters): cleaned missing/high-dimensional data, applied variance filtering and statistical significance testing (t-test/p-value) to identify yield-limiting parameters, and visualized actionable pass/fail trends in Tableau for process-focused root-cause analysis.
