Software Engineer Machine Learning

KAMAKSHI SHAH

270-***-**** *********************@*****.*** LinkedIn

SUMMARY

Software Engineer with 3+ years of experience building scalable tools and backend systems in AI-driven environments. Skilled in Python, data processing, and backend development. Delivered production-ready tools that improved system stability and accelerated AI model workflows. Experienced in cross-functional collaboration and fast-paced R&D environments EDUCATION

Purdue University Aug 2023 - May 2025

Master of Science, Computer Science

Related Coursework: Advanced Data Structures, DBMS, Data Engineering, Analysis of Algorithms, Software Engineering

University of Mumbai Aug 2017 - Jun 2021

Bachelor of Technology, Information Technology

SKILLS

Programming Skills: Java, Python, JavaScript, R, SQL, HTML, CSS, JSON, Postman, Git, C++ (beginner) AI skills: Data Preprocessing, Machine Learning, Natural Language Processing Other: Docker, NumPy, Pandas, Matplotlib, Tableau, Power BI, JIRA, Visual Studio, MS Excel Cloud Technologies: AWS - Lambda, DynamoDB, EC2, EKS, S3; Azure - Container Apps, Communication Services WORK EXPERIENCE

Purdue University

Research Assistant August 2024-December 2024

• Developed Python pipelines to process 20M+ user records, automating data handling which saved over 120 developer hours per month, translating to ~$100K in annual efficiency gains

• Optimized multiple POMDP models using Python, improving decision accuracy by 35% and reducing runtime by 40%, these enhancements significantly boosted the system’s computational performance

• Assisted in troubleshooting and refining PsiTurk-based experiments, ensuring accurate classification and separation of malfunctioning and non-malfunctioning robot data for analysis Procurant

Software Engineer Intern June 2024-August 2024

• Led implementation of several key backend improvements using REST APIs and asynchronous workers, these changes saved 25+ hours of processing time monthly, resulting in ~$30K/year in operational savings

• Processed tens of thousands of product datasets monthly (multi-terabyte scale) using Spark and advanced feature engineering which reduced manual prep time by 40% and improved forecast accuracy by 25%

• Collaborated with a multidisciplinary team to integrate AI models into two critical production systems, ensuring seamless functionality, stability, and alignment with business objectives while mitigating risks Accenture

Software Engineer June 2021-June 2023

• Designed and implemented five innovative solutions to optimize data processing and workflow automation, leading to substantial operational efficiency improvements across multiple projects

• Conducted in-depth analysis and tailored five distinct policy adjustments addressing key technical challenges, driving a 40% increase in client satisfaction within three months based on post-implementation surveys

• Contributed to three major project enhancements, successfully meeting deadlines while conducting eight detailed code reviews and leveraging JIRA for task management, tracking, and team collaboration PROJECTS

QuickBooks Integration Developer January 2024- May 2024

• Developed a Windows Forms application integrated with QuickBooks to enable real-time accounting updates through an intuitive and user-friendly interface, improving workflow efficiency

• Implemented functionality to detect existing user accounts in QuickBooks, displaying “No Change” for unchanged records and “Updated” for modified ones, ensuring data accuracy and consistency

• Utilized the QuickBooks API for seamless data synchronization, optimizing data retrieval, error handling, and overall system performance to enhance responsiveness and operational reliability Google Map Simulator August 2023-December 2023

• Orchestrated the implementation of Dijkstra’s algorithm to analyze and determine the most efficient path across an intricate framework of 15 interconnected states in the northeastern region, ensuring accuracy

• Designed a user-interactive visualization tool to simulate real-time pathfinding, improving the representation of algorithmic efficiency in diverse and complex routing scenarios with dynamic updates

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