Feb 2025 – Jul 2025 · 5 mos · Thane, Maharashtra, India · On-site

• Contributed to infrastructure automation, DevOps, and cloud-native solutions, focusing on deployment, scalability, and observability.
• Deployed and optimized full-stack applications with Docker and Kubernetes, improving efficiency and reducing image sizes by ~90%.
• Built and managed multi-node Kubernetes and OpenShift clusters with Helm, Istio, and NFS storage, enabling reliable and resilient deployments.
• Automated CI/CD pipelines, service mesh traffic monitoring, and centralized logging with Jenkins, Kiali, and ELK stack.
• Implemented and automated messaging/data systems including RabbitMQ, Redis, and Kafka for high-availability setups.
• Integrated AI/ML workflows by developing AngularJS demo apps and building RAG systems with LangChain and Pinecone.
• Conducted case studies and research on cloud databases (Apache Doris vs MySQL), Kubernetes ecosystem, and GPU-based LLM workflows.

Oct 2024 – Sep 2025 · 11 mos · Thane, Maharashtra, India · Hybrid

• Lead of development and database management for the Google Developer Group on campus, organizing workshops and events to enhance technical skills among students.

Jul 2024 – Sep 2024 · 2 mos · Remote

• During the Data Science Master Virtual Internship by Altair, I developed advanced skills in data analysis, predictive modeling, and data visualization. The internship provided hands-on experience with cutting-edge tools and techniques used to derive insights from complex datasets.
• The program focused on:
• Data Preparation and Cleaning: Working with real-world datasets to preprocess and clean data for analysis.
• Exploratory Data Analysis (EDA): Using statistical methods and visualization techniques to uncover trends and patterns.
• Predictive Modeling: Building and fine-tuning machine learning models to make data-driven predictions.
• Data Visualization: Creating compelling visual narratives to communicate insights effectively.
• Altair Tools: Gaining expertise in Altair’s platforms and solutions for data analytics and optimization.
• This internship honed my ability to solve real-world data problems and strengthened my understanding of the data science lifecycle.

Jan 2024 – Mar 2024 · 2 mos · Remote

• During the Google AI/ML Virtual Internship, I gained valuable hands-on experience with cutting-edge AI and machine learning concepts. I worked on practical projects that focused on building, training, and fine-tuning machine learning models using Google Cloud AI tools and services.
• The internship included:
• Leveraging Google Cloud AI Services, such as TensorFlow, Cloud AutoML, and BigQuery ML, to develop scalable machine learning solutions.
• Exploring advanced topics like natural language processing (NLP), computer vision, and recommendation systems.
• Working with large datasets for data analysis, preprocessing, and feature engineering to improve model accuracy.
• Deploying and optimizing ML models on the cloud for real-world applications.
• Understanding ethical considerations in AI development and ensuring responsible AI practices.
• This program enhanced my technical knowledge, problem-solving skills, and ability to develop AI solutions that address real-world challenges effectively.

Sep 2023 – Nov 2023 · 2 mos · Remote

• During the Intelligent Automation Virtual Internship with SS&C, I developed a strong understanding of automation frameworks and strategies for enhancing business processes. I explored and implemented various tools and technologies for process automation, including Robotic Process Automation (RPA), data transformation, and workflow optimization.
• The program focused on:
• Identifying processes suitable for automation by analyzing business workflows.
• Designing and developing automated solutions to reduce manual effort and improve efficiency.
• Integrating data-driven decision-making into automated processes for better insights and outcomes.
• Understanding and applying best practices in automation deployment and testing.
• This internship enriched my technical knowledge of intelligent automation while enhancing my problem-solving and critical thinking skills in a business context.

May 2023 – Jul 2023 · 2 mos · Remote

• During my AWS AI/ML Virtual Internship, I gained hands-on experience with AWS's advanced machine learning tools and services. I explored and worked with AWS services like Amazon SageMaker, Rekognition, Comprehend, and Lex to build, train, and deploy machine learning models.
• I focused on developing predictive models using real-world datasets, which included tasks like data analysis, preprocessing, and optimization to enhance model performance. Additionally, I implemented serverless architectures using AWS Lambda and API Gateway to deploy scalable AI solutions.
• The internship also allowed me to solve real-world business problems through AI, such as sentiment analysis, image recognition, and chatbot development. I strengthened my understanding of cloud resources, collaborated using version control systems, and adhered to best practices for security and scalability.
• This experience greatly enhanced my practical knowledge of AI/ML and prepared me to tackle complex problems in a cloud-based environment.