About Me

🔭 I’m currently working on Speaker Diarization.

🌱 I’m currently learning about MongoDB, Graph VQA, Docker and Kafka.

📫 You can reach me via email at spalkhiw@asu.edu.

📄 Know about my experiences - Resume

⚡ Fun fact: I hold a Bachelor’s degree in Electronics and Communication.

Want to check out my new site under construction - spalkhiw.com

Connect with Me

Work Experience

Software Engineering Intern (AI/ML) @ SIBI LLC | (September 2024 - Present)

Created a dataset of 100+ Text-to-SQL queries to enhance model evaluation, leading to improvement in model accuracy
Designed and implemented an evaluation framework for an internal LLM assistant, incorporating new metrics from EvalKit,improving performance and reliance
Integrated Claude API for Text2SQL evaluation, automating query assessments and enhancing model performance analysis
Reduced token usage by 70% by implementing PromptAgent optimization to address high token costs.
Increased system efficiency by automating SQL performance evaluations on Redshift, minimizing debugging time.
Developed Agentic workflows to build a research agent and LLM-based decision-making for appliance recommendations.

Software Engineering Intern (AI/ML) @ Mirwork Inc | (June 2024 - Aug 2024)

Engineered and optimized data extraction pipelines using YouTube API to process audio, gathering about 70+ hours of content.
Designed, implemented, and maintained MongoDB database to efficiently store and manage extensive audio data sets.
Utilized YouTube data to fine-tune LLM to improve the accuracy and effectiveness of interview assessment tools.
Implemented speaker Darization techniques for structuring data and enhancing LLM fine-tuning for interview assessments.

Software Developer @ Psych for Life | (August 2023 – Present)

Migrated web app from NPM to PNPM, cutting build times and boosting reliability with efficient dependency linking and content-addressable storage.
Achieved 80% reduction in research time by utilizing Prompt Engineering for enhanced efficiency.
Enhanced content management systems by fixing bugs and integrating transcript functionality into the dashboard for streamlined content updates.
Leading integration of AI into the writing process for enhanced efficiency, resulting in a streamlined timeframe.
Orchestrated the development and implementation of custom prompt engineering solutions, resulting in a 50% reduction in process cycle time and a 30% increase in team productivity.
Conducted research, literature reviews, data analysis, and fact-checking for 65+ scientific documents.
Implemented version control, conducted unit testing, and resolved platform bugs in TypeScript, ensuring code stability.
Led collaboration with Luminosity Labs for platform development, which streamlined operations and enhanced communication
Employed caching techniques, resulting in a reduction in 70% decrease in rendering delay and improving website performance
Migrated from NPM to PNPM, reducing package install time by 40% improving dependency management and build reliability.

Software Engineering Intern (CV) @ eInfochips (An ARROW Company) | (Jan 2023 – June 2023)

Led a team of 3 to develop a real-time vehicle detection system for visually impaired individuals, achieving 65.4% average precision and 70.7% for car detection using YOLOv5.
Achieved an 86.5% reduction in resource usage by filter pruning algorithm while maintaining a mean precision of 42%.
Computed CNN models on 16,000 image datasets for vehicle detection, designed to aid visually impaired individuals.
Enhanced real-time safety detection in YOLO by implementing Safety Detection Algorithm, achieving a 25 fps detection rate.
Applied precision-focused strategies and fine-tuned the model using Indian traffic data to enhance accuracy in detecting vehicles within specific environmental conditions.
Collaborated with engineers to streamline training processes, document methodologies, and advance computer vision technology for traffic surveillance and safety applications.

Software Engineering Intern @ Oxvi Respire Solutions | (May 2022 – July 2022)

Developed an Android app to plot real-time values from a prototype ventilator with live Data Visualization.
Integrated Firebase for real-time communication, enabling data transmission between the device and application.
Implemented communication protocols to embedded C components in the prototype for data exchange within a 5ms lag.

Projects

Graph-based QA or Intelligent Traffic Analysis | (July 2022 - Dec 2022)

Extracted 70,000+ frames from traffic videos at 10 fps, enhancing usability and improving VQA system accuracy
Converted 10,000+ questions into GloVe word embeddings, improving the model’s NLP and question understanding capabilities for Scene Graph Generation

Hate Speech Detection using NLP and AI | (Jan 2024 - Present)

Managed a team of 9 in developing and implementing NLP and AI-driven solutions for content filtering.
Delegated research by analyzing over 50+ review articles and research papers to enhance content moderation.

Comparative Analysis of ML Models for Student Grade Prediction | (July 2022 - Dec 2022) Publication

Analyzed and remodeled multiple classifiers, Random Forest and Decision Tree, by optimizing hyperparameters to improve accuracy by 6% and F1-score by 12% for predicting students’ grades.
Demonstrated understanding of Machine Learning fundamentals, including data preparation, model selection, and performance evaluation, with model selection achieving 87.3% accuracy and 0.913 F1-score.
Enhanced hyperparameters tuning using GridSearchCV, leading to a significant 41% improvement in F1 score.
Conducted comprehensive analysis of student grades to identify correlations between different factors and academic performance, aiming to assist educational institutions in improving student outcomes.

Fault Prediction for Combination Circuits | (June 2022 - Nov 2022)

Evaluated and deployed Machine Learning models like Support Vector Classifier, KNN for fault prediction in circuits.
Accelerated testing processes and slashed time by 25% spent on fault testing and verification.
Accomplished 36% accuracy while predicting faults, which was improved to 54% by Data Engineering.
Optimized performance by Feature Engineering, leading to a 40% reduction in computation time.

Movie Booking System | (Sept 2021 - Nov 2021)

Collaborated on a C program utilizing Arrays and Queues to simulate theater seating arrangement, allowing users to choose seats and rows and display the total amount due.

Education

M.S., Computer Science - Arizona State University (May 2025)
B.Tech., Electronics and Communication - Nirma Institute of Technology (May 2023)

Technical Skills

Languages

C++, Python, C, Perl, Shell, Kotlin, Linux, Java, Matlab

Tools & Frameworks

HTML, CSS, YOLO, OpenCV, Flutter, Kafka, Apache, Spark, Docker, SQL

Developer Tools

AWS, Git, Anaconda, Spyder, Linux, IntelliJ, VSCode, Android Studio, LaTeX, PowerBI, Tableau, Firebase, Whisper

Libraries

NumPy, Pandas, Keras, Scikit-learn, Torch, TensorFlow, PyTorch, Neural Networks, Pyannotate, Seaborn

Publications

Analysis of Machine Learning algorithms for predicting student grade - Springer Journal of Data, Information and Management Publication
Fire Detection and Control Systems - STM Journals, International Journal of Radio Frequency Design Publication

Sparsh Palkhiwala