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.
