Full Stack Software Development

Siddharth Shah

*******************@*****.*** • 412-***-**** • linkedin.com/in/siddharthgautamshah

EDUCATION

Carnegie Mellon University Pittsburgh, PA

Masters of Science in Electrical and Computer Engineering, GPA - 4.0/4.0 May 2025 Relevant Courses - Introduction to Deep Learning, Machine Learning in Production, Foundations of Computer Systems, Computer Vision(S25), Deep Generative Models(S25), Estimation, Detection and Learning Manipal Institute of Technology Manipal, India

Bachelor of Technology in ECE with a minor specialization in Data Science, CGPA - 8.86/10 July 2022 EXPERIENCE

Microsoft Hyderabad, India

Software Development Engineer Jan 2022-Jan 2024

• Collaborated with senior engineers to develop the Compliance Automation platform, a SaaS web application used internally for reviewing User Accesses on Azure portal.

• Built and maintained three full-stack applications with React (UI), React Native, C#, and Node.js, supporting both frontend and backend architecture. Developed and integrated APIs with SQL and CosmosDB databases, participating in all phases of the software development life cycle (SDLC), from requirement gathering to deployment and monitoring.

• Automated CI/CD pipelines with Azure DevOps, improving deployment efficiency by 30%, with unit testing in xUnit.

• Developed Power BI and Grafana dashboards to visualize access trends, reducing audit time by 50%.

• Orchestrated ETL pipelines using Azure Data Factory, integrating Spark and Kafka for real-time data transformation.

• Worked in Agile and Kanban environments to deliver iterative software enhancements and feature deployments.

• Troubleshot and resolved production issues, reducing MTTR by 20% through real-time monitoring and on-call support Parkyeri Mumbai, India

Development Intern Jul 2021-Oct 2021

• Developed real-time backend infrastructure with Django and GraphQL for a smart parking system across 50+ locations.

• Built and deployed hardware integration (Raspberry Pi + sensor modules) for visual and sensor-based spot detection, involving time-indexed image data processing, OCR for object recognition, and real-time template matching.

• Added risk management practices by implementing incident monitoring, access audits, and compliance dashboards.

• Secured authentication and containerized the stack using JWT, Docker, and AWS ECS; ensured HA and performance.

• Created detailed technical documentation for APIs, system designs, and deployment procedures. RESEARCH EXPERIENCE

Carnegie Mellon University Pittsburgh, PA

Research Assistant at Optimization, Probability and Learning (OPAL) Lab May 2024-Present Project: Federated Learning for Large Language Models Using LoRA

• Developed scalable federated learning pipelines using LoRA/HetLoRA for distributed edge clients.

• Implemented adaptive sync and aggregation strategies (SVD, padding, FedAdam) to reduce system overhead.

• Focused on distributed systems and memory optimization in low-bandwidth environments. PROJECTS

Carnegie Mellon University

Building a Scalable MLOps-Driven Movie Recommendation Service Aug 2024-Dec 2024

• Designed and implemented a scalable, cloud-native movie recommendation system serving 1M users and 27K movies, deploying this application in AWS.

• Developed frontend in Vue and backend REST APIs in Go with load balancing and asynchronous processing, ensuring sub-800ms response time, exploring content-based filtering using metadata and visual elements.

• Integrated Kafka for real-time data streaming, Redis caching for retrieval, and PostgreSQL for persistence.

• Deployed containerized Microservices using Docker and Kubernetes, using workflows in Airflow with CI/CD pipelines in Jenkins ensuring automated updates.

Building a Computer System from Scratch: Memory, Shell, Proxy, and Caching Jan 2024-May 2024

• Memory Allocator & Shell: Wrote a custom malloc using segregated free lists and built a Unix-style shell with job control and signal handling.

• Web Proxy & Caching: Developed a multi-threaded proxy with LRU caching, cutting response times by 35%.

• Cache Simulator: Implemented a set-associative cache simulator to profile and optimize memory usage. SKILLS

Languages: C++, C, Python, Java, TypeScript, SQL, C#, Kotlin, Go Systems & Infra: Distributed Systems, File Systems, Networking, Unix/Linux, Memory Management, Web Proxies Cloud & DevOps: Azure (AZ-900, AZ-204), AWS, GCP, Docker, Kubernetes, CI/CD (Azure DevOps, Jenkins), Prometheus Tools: Spark, Kafka, Airflow, Redis, CosmosDB, OpenCV, PostgreSQL, PyTorch, GraphQL, ETL Pipelines, Observability

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