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Abhinav Jindal

Software Engineer

CV

About Me

Welcome to my portfolio! I'm a software developer with a passion for applied machine learning. My professional experience combines robust backend development, microservices, NLP, and computer vision. I hold a strong academic background and believe great software addresses both technical excellence and real-world impact. Explore my projects and let's connect!

Experience

PayPal, San Jose, California

Fullstack Software Engineer

Full-stack software engineer embedded in the Pricing and FX Payments space, working across the entire stack, from Java backend services handling millions of low-latency transactions daily, to React-based merchant-facing workflows. I took ownership of complex, cross-functional initiatives rather than just executing tickets: I led the end-to-end expansion of PayPal's Merchant Hub into Middle Eastern and African markets, aligning engineering and business stakeholders to unlock $17M in new annual revenue. Alongside this, I am also working on AI initiatives to streamline triaging and debugging flows. I designed and shipped a multi-agent orchestration system for FX transaction analysis, cutting live support turnaround time by 75%. It was one of the first production deployments for Payments org using Google ADK, CrewAI, MCP, and RAG, and required both technical depth and the ability to drive adoption across teams.

Technologies Used: Java, SpringBoot, React, Next.js, CrewAI, RAG, MCP, Python, Harness.

USC Information Sciences Institute, Los Angeles, California

AI Engineer

Built production-grade AI agentic onboarding assistant adopted by 100+ new hires. I owned the end-to-end Q&A workflow, delivering contextual, personalized guidance that accelerated time-to-productivity by 60%. Beyond the pipeline, I designed the prompt engineering layer from the ground up, experimenting with zero-shot and few-shot in-context learning across both open- and closed-source LLMs to generate accurate, code-centric responses. On the infrastructure side, I deployed a FastAPI-based ML inference service, tuning retrieval performance against evaluation metrics like Recall@K and MRR to ensure the assistant held up at scale.

Technologies Used: PyTorch, GPT, CodeLlama, Python, Flask, Faiss, Elasticsearch, FastAPI, CodeT5.

Intel Corporation, Santa Clara, California

Software Engineering Intern

Improved performance of the Cobalt, Intel's graphics simulation module, effectively boosting CPU frequencies while fortifying against potential future regressions. Identified and improved suboptimal algorithms, harnessing tools such as V-Tune to pinpoint and address bottlenecks leading to a 300% surge in CPU frequencies. I also devised scripts for generating visualization plots and scrutinizing upcoming commits, enabling vigilant monitoring to detect regression-inducing changes.

Technologies Used: C++, Python, VTune, Visual Studio.

The D.E. Shaw Group (Schrödinger), Hyderabad, India

Software Developer I

Worked for the LiveDesign team, a platform for drug discovery and material science, where I managed a small engineering team, led projects, and optimized features. As the engineering lead, I was responsible for the Admin Panel service, which assists in configuring user roles and project permissions. I was also part of the LDLearning team to integrate LiveDesign with the machine learning backend. I revamped the CI/CD pipeline and migrated these services to the microservice architecture using Docker, Jenkins, and Kubernetes for streamlined development, testing, and deployment. Additionally, I implemented ACL enhancements, model retraining, JWT authentication, SSO, and other major features, showcasing my versatile impact on LiveDesign's growth.

Technologies Used: Java, Python, Django, React, JavaScript, Flask, Docker, Kubernetes, RQ.

Linkbal, Tokyo (Japan)

Data Science Intern

Enhanced the accuracy of Event Cancellation Prediction, aiming to improve the event ranking process on the company's website. This aimed to bolster customer confidence in the company's service quality while mitigating losses attributed to refund procedures. To achieve this, I analyzed features within historical event data, employing statistical analysis methods, and formulated predictive models utilizing CatBoost and neural networks.

Technologies Used: Python, Jupyter Notebook, OpenCV, Keras, CatBoost.

Education

University of Southern California (USC), Los Angeles

August 2022 - May 2024

Master of Science (Honors), Computer Science

Indian Institute of Technology (IIT), Ropar

July 2016 - May 2020

Bachelor of Technology, Computer Science and Engineering

Projects

Ebay Search Portal

Developed a web and android application for efficient products search based among millions of products filterable on certain criterias. The application was connect to a nosql database and the whole system was deployed using GCP

Technologies Used: React, Node.js, Flask, JavaScript, Android Studio, Kotlin, MongoDB, GCP.

Code | Website

BooHu Entity Posession Game

Designed and engineered a 2D platformer allowing entity posession and their ability usage to navigate complex challenges to complete a level. Conducted comprehensive user testing sessions to collect feedback, and by adhering to Agile methodologies, we enhanced gameplay, resulting in iterative improvements.

Technologies Used: Unity, C#, Google Analytics.

Code | PDF | Video | Play

Enhanced Formality Transfer and Offensive Language Mitigation

Executed fine-tuning of the OpenAI Large Language Model (LLM), GPT-3, using a specialized dataset to metamorphose sentences into a polished and considerate formal structure. Introduced a proprietary dataset, with more than 500 instances, attaining fluency and performance on par with human levels.

Code | PDF

Medical Segmentation Decathlon

Worked on generalizability of 3D medical image segmentation across 10 different tasks and adapting to unseen task without human annotations. Used a U-NET FCN-based model along with 3D image processing techniques, producing results comparable to state-of-the-art.

Code | PDF
View More Projects

Audio-Video Compatability

Predicting the compatability score of multiple videos to an audio input and sorting the videos according to their appropriateness to the audio. We used the number of youtube views as a metric for our prediction. We extracted various faetures from the audio like scenes, emotions, pacing and matched them to audio features like beats and emotions. We compared and combined these values to get our predicted score.

Code | PDF

Generative Chatbot

For generating intelligent responses with different responses everytime and helping continue the conversation, we built a chatbot using a Sequence to Sequence model. We further improved it using embedding layers for compactness and attention, and teacher forcing mechanism.

Code | PDF

Skills

Achievements

Full Scholarship at IIT, Ropar

July 2016

Joint Entrance Exam (JEE) Advanced 2016

State Rank 1

April 2016

Joint Entrance Exam (JEE) Mains 2016

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