Pulkit Chawla
Data Science & AI Engineer
Eindhoven
Summary
Master’s student in Data Science and Artificial Intelligence with hands-on experience in big data platforms, cloud infrastructure, and full-stack development. Contributed to HPC research platforms and industry-scale systems at Oracle and Qubole, building scalable APIs, microservices, and cloud-native big data services. Eager to apply this blend of research and engineering expertise in a dynamic environment that fosters both organizational impact and career growth.
Overview
8
8
years of professional experience
Work History
Student Software Developer
EINDHOVEN ARTIFICIAL INTELLIGENCE SYSTEMS INSTITUTE
10.2023 - 12.2024
- Contributed to the Digital Twin Lab initiative under EAISI, TU/e’s hub for AI innovation and collaboration between internal departments, and industry partners.
- Designed and implemented a robust FastAPI-based API layer to support the semantic infrastructure for digital twin applications.
- Modeled and encoded ontologies to semantically represent sensor data, such as temperature and humidity readings sourced from TU/e building systems, enabling structured storage in a graph database and rich querying capabilities.
- Collaborated with internal stakeholders across TU/e divisions to consume real-time sensor feeds, ensuring the API and ontology structure met practical data needs, and semantic consistency.
Member of Technical Staff
ORACLE CLOUD INFRASTRUCTURE
01.2021 - 08.2023
- Joined as one of the early engineers in the inception of Oracle’s OCI Big Data Service team, contributing to the end-to-end design and development of the platform from scratch. Integration and maintenance of HBase and its connectors with Oracle's Hadoop distribution.
- Architected and deployed Oracle’s custom Hadoop distribution using Apache Bigtop stacks, with Ambari for provisioning, cluster management, and monitoring.
- Built React-based user interfaces for the OCI Big Data console, delivering intuitive cluster lifecycle management, and monitoring dashboards for customers.
- Designed and implemented CI/CD pipelines in TeamCity, automating build, test, and deployment workflows to support continuous delivery across distributed systems.
- Collaborated directly with senior architects and product managers to define system requirements, prioritize features, and deliver the first production release of Oracle’s Big Data Service.
Member of Technical Staff
QUBOLE
01.2018 - 01.2020
- Built and maintained cloud-based cluster management software in Python, orchestrating big data engines, including Apache Spark, Hadoop, and Presto, in multi-cloud environments.
- Designed and developed a microservices suite from scratch in Molecular.js, enabling cluster administration, user management, and efficient troubleshooting workflows for enterprise customers.
- Integrated monitoring and diagnostics pipelines using Datadog, Ganglia, and Splunk, improving visibility into distributed cluster health, and reducing incident resolution times.
- Partnered with support and operations teams to investigate production issues, performing log and metric analysis to deliver actionable insights to customers.
Engineering Intern
QUBOLE
01.2018 - 07.2018
- Automated test cases for Qubole’s cloud-agnostic cluster management framework using Pytest and Selenium, increasing test coverage and reducing manual QA overhead.
- Contributed to release deployment and validation, ensuring stability and quality across Qubole’s distributed Big Data platform.
- Built and enhanced CI/CD pipelines, enabling seamless code build, test, and deployment workflows across teams.
Research Intern
Illinois Institute of Technology
05.2017 - 07.2017
- Conducted research under Prof. Joohee Kim on pedestrian detection for self-driving cars using Single Shot MultiBox Detector (SSD) networks.
- Implemented and fine-tuned SSD on the Caffe framework with a VGG-16 backbone, applying modifications for improved detection accuracy and speed.
- I trained and evaluated models on the Caltech Pedestrian Dataset, converting data into LMDB format for efficient GPU-accelerated training.
- Achieved a miss rate of 41.39%, outperforming several state-of-the-art detectors of that period, while maintaining real-time inference speeds (~41 FPS) on an NVIDIA GTX 1080 Ti.
Education
Master of Science - Data Science and Artificial Intelligence
Eindhoven University of Technology
Eindhoven 04.2001 -
Bachelor of Engineering - Information Technology
Birla Institute of Technology
India05-2018
Skills
Python, C, JAVA / Javascript / HTML
Deep Learning and CNN Networks
Generative AI
Distributed Microservice Architecture
Big data engines (Hadoop, Spark)
Data Mining
System integration and automation
Splunk
undefinedTimeline
Student Software Developer
EINDHOVEN ARTIFICIAL INTELLIGENCE SYSTEMS INSTITUTE
10.2023 - 12.2024
Member of Technical Staff
ORACLE CLOUD INFRASTRUCTURE
01.2021 - 08.2023
Engineering Intern
QUBOLE
01.2018 - 07.2018
Member of Technical Staff
QUBOLE
01.2018 - 01.2020
Research Intern
Illinois Institute of Technology
05.2017 - 07.2017
Master of Science - Data Science and Artificial Intelligence
Eindhoven University of Technology
04.2001 -
Bachelor of Engineering - Information Technology
Birla Institute of Technology
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