Applied AI Engineer

I architect and deliver event-driven serverless pipelines using AWS Lambda, S3, Step Functions, and DynamoDB to automate ingestion and transformation of large-scale financial datasets. These systems process millions of records daily with near-zero manual intervention. I also build high-performance Python and FastAPI microservices using asynchronous I/O and Pydantic validation for real-time distributed financial-data processing.

I developed data-reconciliation frameworks that validate integrity across more than five upstream and downstream systems, reducing discrepancies by approximately 20%. I automate infrastructure with reusable Terraform modules, reducing environment provisioning time by 60%, and maintain Dockerized ECS/Fargate services with Jenkins CI/CD, automated testing, image scanning, and blue/green deployments. I also implement CloudWatch and QuickSight observability dashboards, build resilient SQS/SNS and IBM MQ messaging integrations, integrate investment systems with LUSID, and maintain 85%+ test coverage using pytest and moto.

I designed and implemented RESTful APIs and microservices with Spring Boot for a scalable e-learning platform. Through query tuning and backend optimization, I improved API response time by 25%.

I collaborated with React frontend and QA teams to deliver full-stack features on schedule. I maintained API contract documentation using Swagger and OpenAPI while supporting efficient integrations with external systems.

Bachelor of Technology in Computer Science Engineering.