Ritwick Chava

Experienced Data engineer and Python developer with 5+ years of expertise in developing scalable cloud solutions and deploying end-to-end machine learning models. Proven track record of optimizing cloud infrastructure, building APIs, and enhancing DevOps workflows. Skilled in AWS Sagemaker, Azure ML Studio, Kubernetes, Docker, and LLMs with a passion for delivering high-performance AI solutions. Strong collaboration skills, mentoring experience, and Microsoft-certified technical proficiency.

Data Engineer at VRN Technologies (2024-07 – Present)

Client: USAA. Designed and maintained end-to-end batch and streaming data pipelines on AWS using AWS Glue, Amazon S3, Amazon Redshift, and Amazon Kinesis, processing millions of records daily across transactional and event-driven datasets.

• Designed and maintained end-to-end batch and streaming data pipelines on AWS using AWS Glue, Amazon S3, Amazon Redshift, and Amazon Kinesis, processing millions of records daily across transactional and event-driven datasets.
• Built and supported scalable Member Data REST APIs using AWS Lambda and API Gateway, ensuring low latency, high availability, and data accuracy for multiple internal and external consumers.
• Engineered near real-time streaming solutions with Spark Structured Streaming on AWS Glue / EMR, handling 5M+ events per day, implementing replay mechanisms, watermarking, and SLA-driven reliability.
• Implemented robust data modeling and transformation logic, including Type 2 SCDs, incremental loads, and deduplication, improving downstream data consistency and consumer trust.
• Developed Python-based data frameworks (PySpark, Pandas) for data validation, anomaly detection, and feature-ready dataset preparation, enabling AI/ML and advanced analytics use cases.
• Optimized Amazon Redshift and Athena performance and cost through query tuning, partitioning, workload management, and resource governance, achieving approximately 15% reduction in monthly compute costs.
• Implemented CI/CD and operational best practices using Git, IAM, CloudWatch, and automated monitoring, improving observability and production support readiness.

AI/ML Intern at Evergent Technologies Inc. (2023-06 – 2024-04)

Developed and deployed machine learning models on Azure ML Studio and AWS SageMaker for seamless integration into production systems, ensuring 99.9% availability of services.

• Developed and deployed machine learning models on Azure ML Studio and AWS SageMaker for seamless integration into production systems, ensuring 99.9% availability of services.
• Implemented LLM-based chatbots for automated user interactions, improving customer engagement by 20% and reducing operational costs.
• Collaborated on explainable AI (XAI) solutions, enabling stakeholders to interpret neural network outputs and drive data-driven decisions.
• Optimized multi-threaded APIs using Docker and Gunicorn, reducing response latency.
• Partnered with engineering teams to automate troubleshooting using GitLab, leading to 25% faster resolution of production issues.
• Conducted performance benchmarking for deployed models, achieving a 15% improvement in model inference speed through optimization.

Assistant System Engineer at Tata Consultancy Services (2021-06 – 2022-06)

Microsoft Business Unit. Onboarded Python-based AI/ML applications into Azure cloud environments, optimizing workloads for Kubernetes clusters and virtual machines to ensure scalability and availability.

• Onboarded Python-based AI/ML applications into Azure cloud environments, optimizing workloads for Kubernetes clusters and virtual machines to ensure scalability and availability.
• Automated infrastructure provisioning through Terraform, reducing setup time by 40%.
• Designed and managed CI/CD pipelines using Azure DevOps, enabling automated deployments across multiple environments.
• Implemented IAM policies and access control using Azure Active Directory, improving security compliance for data scientists and engineers.
• Collaborated with cross-functional teams to integrate new features into existing applications based on client feedback, enhancing functionality and user satisfaction.
• Mentored new team members on AI/ML workflows and Python-based data processing, fostering a knowledge-sharing culture.
• Reduced cloud operational costs by 20% through resource optimization and auto-scaling.
• Improved application response time by 15% by fine-tuning Kubernetes clusters and Python APIs.

Data Analyst Intern at Wilco Source (2020-04 – 2021-05)

Built and maintained ETL pipelines in Python, ensuring data availability for ML models by automating data ingestion processes.

• Built and maintained ETL pipelines in Python, ensuring data availability for ML models by automating data ingestion processes.
• Led the deployment of AI/ML models on Azure, ensuring seamless scalability and performance reliability for production environments.
• Collaborated on AI-driven strategic initiatives, aligning cloud infrastructure solutions with business objectives.
• Managed version control systems using GitLab, ensuring smooth collaboration and code management.
• Conducted performance tuning of cloud applications, achieving 18% improvement in reliability and uptime.

Master of Science in Financial Technology and Analytics – University of Texas at Dallas (2022-08 – 2024-05)

Bachelor of Technology in Computer Science Engineering – Amrita Vishwa Vidyapeetham (2017-06 – 2021-05)