Software Engineering Engineer
Resume:
Pulkit Dongle
Piscataway, NJ *****
******.******@*****.***
www.linkedin.com/in/pulkitdongle/
SKILLS
Programming Languages: Python C++ Java Node.js C MATLAB Typescript.
Software & Frameworks: Flask Django FastAPI Spring Express.js Angular.js D3.js TensorFlow OpenCV.
Machine Learning Tools: PyTorch Scikit-Learn NumPy Pandas.
Database Management: MySQL SQLite DB2 MongoDB.
Tools: Git GitHub Kubernetes Spark Jenkins Jira pytest.
Web Technologies: JavaScript HTML CSS Angular J2EE jQuery AWS. WORK / RESEARCH EXPERIENCE
Qualcomm - Bridgewater, NJ 07/’20 – 01/’24
Software Engineer – Channel Emulation (Panda Control Server)
Led the design and development of software components for wireless communication systems using object-oriented programming skills in Linux environment (CentOS, RHEL) on Qualcomm cloud.
Collaborated with cross-functional teams, including hardware engineers and system architects, to define software requirements, project milestones and implement new features for Panda Control Server.
Extensive experience in designing and implementing REST APIs, ensuring scalability, security, and robustness.
Used Automation and Integration processes, utilizing tools like Jenkins for continuous integration and continuous deployment
(CI/CD).
Experienced in Agile/SCRUM methodologies, participating in sprints, stand-ups, and sprint planning sessions to deliver iterative software solutions.
Made use of Git/GitHub for version control, collaborating on projects, and managing code repositories effectively.
Improved system reliability by conducting extensive testing and debugging, leading to a 20% reduction in post deployment issues and enhancing overall software stability.
Effectively addressed and resolved customer issues related to production deployments, ensuring minimal downtime, delivering prompt and precise solutions to elevate overall customer satisfaction. [C++, Python, Agile, CI/CD Pipeline, REST APIs, Pytest, NumPy, Linux, Git].
Human Language Analysis Beings Lab - SBU, NY 06/’19 - 05/’20 Graduate Research Assistant - Advised by Prof. H. Andrew Schwartz
Built an open-source ORM framework for Differential Language Analysis Toolkit (DLATK) by creating a generic implementation of the connection with different DBMS while abstracting the SQL query building process.
Increased developer productivity by 20% through reduced boilerplate code and simplified database management using abstraction layers.
Improved code maintainability and reduced complexity by 10% through Object-Relational Mapping (ORM). Achieved this by:
Eliminating 70 % of manual SQL queries: Simplifying complex data interactions with built-in ORM functionalities, reducing error- prone queries and reliance on database-specific syntax.
Decreasing code size by 10 %: Replacing verbose SQL statements with concise object-oriented constructs, enhancing readability, and reducing overall project size. [Python, MySQL, SQLite] Thinking Machines – Ujjain, India 08/’17 - 11/’18
Software Engineer Intern
Web Application Server Framework: Built a Web Container for Node.js. Handled parsing of request/ response/ cookies/ sessions along with the flow management.
Reduced response time by 24%: Implemented custom optimizations like caching strategies and session management leading to faster user experiences.
Boosted developer productivity by 25%: Eliminated boilerplate code with pre-built functionalities, enabling developers to focus on core logic. [Node.js, Express.js]
Face Recognition Engine Abstraction for Attendance Monitoring System: Developed a supervised learning-based attendance monitoring system. Used OpenCV’s LBPH face recognizer and prebuilt Haar cascade classifier model for frontal face detection.
Boosted attendance tracking accuracy by 60%: Eliminating issues like worn-out fingerprints or proxy punching, leading to more reliable data.
Eliminated physical contact: Touchless system minimizes germ spread and promotes safety, especially during pandemics.
Increased user convenience by 80%: Faster and smoother process compared to fingerprint scanners, improving user satisfaction.
[Python3, C#, OpenCV, Django, MongoDB].
Java Generic Socket Server: Users can easily convert any java-based desktop application to a network application by following certain specified set of rules. [Java, Spring].
Eliminates socket programming expertise: By abstracting away low-level network complexities, the project empowers developers with little or no socket programming experience to create network applications.
Enables network-based communication: Transforms existing desktop applications into network-accessible solutions, expanding their reach and potential user base.
Facilitates distributed computing: Establishes the foundation for building scalable and distributed systems where communication and collaboration across machines is essential.
Academic Projects
Text Summarization and Scoring 09/’19 - 12/’19
Summarized students’ comments on the SBU’s courses using Skip-Thoughts Encoder-Decoder Model
Scored the summary by using NLTK’s sentiment analysis package to obtain a grade for the course. [Python, NLTK] Data Science 08/’19 - 10/’19
Housing Price Prediction: Finding and analyzing correlations, predicting the prices of houses using different machine learning models.
IEEE Fraud Detection: Data Cleaning and Exploration. Analyzing transaction details to generate interesting insights, feature engineering and creating a ML model to predict fraud transactions. [Python3, IPython Notebook, Sklearn, Pandas, Matplotlib, Seaborn]
Visual Analytic Tool 02/’19 - 05/’19
Implemented data sampling, K-means clustering, and dimensionality reduction using Principal Component Analysis (PCA).
Visualized Human Freedom Index data using d3.js for analyzing various factors affecting human freedom around the world.
[JavaScript, HTML, CSS, Python, Pandas, Flask]
Education
Stony Brook University, NY 01/’19 – 05/’20
Master of Science, Computer Science
Ujjain Engineering College, India 08/’14 – 05/’18
Bachelor of Engineering, Computer Science.
AWARDS AND CERTIFICATIONS
Machine Learning by Stanford University on Coursera 11/’18
Neural Networks and Deep Learning by deeplearning.ai on Coursera 11/’18
Contact this candidate