Software Engineer Machine Learning

Aslam Shaik

317-***-**** # *******@******.*** ï linkedin.com/AslamShaik § github.com/AslamShaik

Education

Purdue University Expected 2025

Master of Science in Computer and Information Science (GPA: 3.8 / 4.00) Indianapolis, Indiana

• Coursework: XAI, DataBases, Machine Learning, Distributed Systems, Computer Networks, Algorithms Technical Skills

Languages: Java, Python,c++,JavaScript.

Technologies: AWS, SpringBoot, Node.js, Kafka, Flask, Express.js, PyTorch, Textract, MongoDB, SQL, Linux, Git, Redis. Concepts: System Design & Microservices Architecture, Scalability & High Availability, Data Structures & Algorithms, RESTful APIs, Backend Frameworks, Continuous Integration & Deployment (CI/CD), Big Data Technologies & Databases, ML Algorithms & Data Pipelines, Problem-Solving & Algorithmic Thinking, Agile & SDLC, Collaboration & Cross-Functional Teamwork, Technical Leadership & Mentorship, Caching Strategies, Concurrency & Parallel Processing. Experience

Indiana University Luddy School of Informatics, Computing, and Engineering August 2024 – present Graduate Researcher - Big Data in AI, Teaching Assistant - Software Systems.

• Research Assistant (NSF-Funded Project)

Led research and development of scalable Big Data backend systems for an NSF-funded project, optimizing data pipelines to process terabyte-scale healthcare datasets with a 99% reduction in latency, Optimized query performance by implementing indexing, partitioning, and caching strategies, reducing query response times by 60% and supporting real-time analytics. Engineered distributed architectures and fault-tolerant storage solutions leveraging AWS S3 and DynamoDB, integrating machine learning models to improve the accuracy of healthcare diagnostic prediction by 25%, while achieving 99. 99% system uptime and improved scalability.

• Graduate Teaching Assistant

Led and mentored 100+ students, improving concept comprehension by 30% through structured lesson plans, interactive teaching methods, and clear, intuitive explanations. CAMP Systems International July 2022 – December 2023 Software Engineer, Machine Learning, Backend

• Led the development and deployment of a real-time ML Classification system, collaborating with cross-functional teams to design a scalable data pipeline for ML performance evaluation. Designed the backend while mentoring an intern to build the frontend, ensuring seamless integration and production readiness. Implemented automated model evaluation workflows, improving deployment efficiency by 60% and maintaining 99.9% uptime.

• Architected and deployed a high-throughput AWS-based data pipeline (S3, Lambda) to compute ML model performance metrics, reducing latency by 30% and increasing data processing efficiency by 2x. Developed an NLP-based document classification system, reducing manual intervention by 70% and operational costs by 50%, integrating Flask, AWS Lambda for low-latency inference and scalability. MTX Group Inc January 2022 – June 2022

Salesforce Developer

• Developed custom Salesforce applications for banking and university clients, optimizing dashboards and automating workflows with Apex, improving data visibility by 30% and operational efficiency by 20%. Eunimart Omnichannel Pvt Ltd. June 2021 – December 2021 Backend Developer

• Designed and deployed a microservices architecture using Node.js and RabbitMQ, enabling real-time tracking of inventory levels and automating reordering processes, leading to a 40% decrease in manual data entry errors. Projects

Real-Time Chat Application Node.js, Socket, MongoDB, Kafka, Git October 2024

• Designed and implemented scalable system architecture using Node.js and WebSockets, optimizing message latency by 30% and ensuring reliable delivery through efficient transport protocols. Built a high-performance system capable of handling over 10,000 concurrent users, reducing server-client interaction time by 25%. Enhanced security by integrating advanced encryption, and Proposed a modular architecture that simplified future feature integration, maintaining 99% code clarity and scalability.

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