Engineered end-to-end ML workflows on AWS SageMaker, managing model training, artifact versioning, and deploying auto-scaling endpoints for real-time inference.

Built robust data preparation pipelines using AWS Glue, automating the transformation of raw S3 data into structured datasets optimized for model training.

Leveraged Terraform and CloudFormation to provision and manage multi-cloud AWS environments (Dev/Prod), ensuring 100% infrastructure consistency and eliminating configuration drift.

Developed high-performance inference services using FastAPI, integrating PyTorch/TensorFlow models with backend microservices.

Collaborating with cross-functional teams in Agile/Scrum environments and conducting code reviews and peer mentoring.

Designed real-time data ingestion pipelines using Kafka and handled streaming use-cases for AI-driven text classification models.

Improved system scalability by migrating legacy monolithic workloads to Docker-based CI/CD pipelines on AWS.

Collaborated on recommendation engine development, utilizing Scikit-learn for predictive analytics to enhance user engagement.

Contributed to AI-driven solutions for text classification and recommendation engines.

Improved system performance by migrating workloads to AWS and introducing Docker-based CI/CD pipelines.

Contributed to the design and development of client-facing web applications using Python (Django) and React.

Collaborated with clients to define requirements, ensuring timely delivery of scalable solutions.

Built data-driven dashboards and reports for business analytics using PostgreSQL and AWS QuickSight.

Implemented secure authentication and authorization mechanisms using JWT and OAuth.

Gained exposure to cloud deployments, containerization (Docker), and Agile methodologies.