Software Engineer Data Processing

Praveen Dharavath

+1-380-***-**** - ********************@*****.*** - LinkedIn

TECHNICAL SKILLS

Programming Languages: Python, Java, C++, C.

Libraries and Tools: PyTorch, Sklearn, Pandas, NumPy, NLP, NLTK, OpenCV, Git, LLMs. Databases: Oracle, MySQL, Microsoft SQL, PostgreSQL. Web Frameworks: Django, Flask, FastAPI.

Web Technologies: Html, CSS, Bootstrap, jQuery, Vue.js, JavaScript, React, Angular, Node.js. Cloud & CI/CD Tools: AWS, Azure services, Jenkins, Azure SQL, Git, Kafka, Docker, Kubernetes. WORK EXPERIENCE

Software Engineer

Macy’s, Houston, Texas Jul 2024 - Present

In a recent project, I designed and implemented AI-driven solutions by using Python integrating machine learning models with cloud-based APIs, enabling scalable deployment and real-time data processing. This approach streamlined backend operations, improved data processing efficiency, and enhanced overall system performance.

Designed and optimized complex queries and procedures in PostgreSQL, Worked with High-Performance Computing (HPC) environments, optimizing AI/ML workflows. Optimized SQL queries and database procedures in PostgreSQL, reducing query execution time by 40%.

Integrated CI/CD pipelines and version control in Git, ensuring smooth deployment and code management. Leveraged containerization tools like Docker and orchestration tools like Kubernetes for deployment.

Collaborated with UX Design Teams to enhance front-end experience and ensure seamless API integration. Experienced with testing frameworks like JUnit and Pytest, ensuring code reliability and robustness. Developed and maintained real-time data processing pipelines using Apache Kafka, enabling efficient handling of high-volume data.

Applied Azure security best practices, including identity management, encryption, and network security groups, to protect sensitive data. Developed and maintained RESTful and GraphQL APIs for seamless data exchange between services. Software Engineer

Logitech, Hyderabad, India Jun 2021 - Sep 2022

Developed applications using Python Django framework and utilized SQL Alchemy for comprehensive SQL management. Automated backend tasks with Python scripting, reducing manual operations by 50%.

Integrated AWS Lambda and S3 for serverless file processing, cutting operational costs by 15K dollars annually. Automated and optimized database operations by developing PL/SQL procedures, streamlining data processing, and integrating with CI/CD pipelines.

Using Azure DevOps, I established and managed Continuous Integration and deployment. Implemented robust security and resiliency measures by designing fault-tolerant microservices, applying encryption standards, and enforcing access controls to safeguard sensitive data.

Integrated NoSQL databases like MongoDB and DynamoDB to handle high-throughput data processing, ensuring scalability and performance optimization. Knowledge of Unix scripting for automation and process optimization. Jr Software Engineer

Prog Solutions, Hyderabad, India Aug 2020 - May 2021

Initiated the Django Rest Framework where RESTful APIs are implemented, enabling smooth data transfer between the frontend and backend components. Built a customer-facing dashboard using React.js and Node.js, reducing page load time by 20%.

Designed and optimized SQL schemas for improved data integrity and performance. Performed post-implementation support by examining code and logs to resolve production issues efficiently.

Managed SQL databases within Django applications, ensuring optimal query performance, data integrity, and effective schema design. Contributed to code reviews and mentored two interns, enhancing team code quality and streamlining. PROJECTS

Quora Questions Pair similarity — In addressing the binary classification problem of identifying duplicate questions on Quora, I employed T-SNE for high-dimensional data visualization, enabling a clearer understanding of question similarities. To enhance the neural network’s comprehension, I utilized TF-IDF weighting for Word2Vec embeddings, allowing the model to learn intricate word connections from a substantial text corpus. This approach aimed to improve the accuracy of predicting duplicate questions.

Business Meeting Summary Generator — A machine learning model summarizes the huge information which is in text format and gives a summary of the document word tokenization and documented based on word frequency. The summary generator uses NLTK for text preprocessing (tokenization, stopword removal, frequency analysis), Python’s standard libraries (heapq, string, data structures) for sentence scoring/selection, and rule-based NLP to prioritize key sentences without machine learning. EDUCATION

Franklin University Ohio, USA

Masters - Information Technology Sep 2022 - May 2024 Jawaharlal Nehru University Hyderabad Hyderabad, India Bachelors - Computer Science Jun 2017 - May 2021

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