AWS Certified Developer Associate with 2+ years of experience designing and implementing scalable applications, data pipelines, and cloud-native solutions. Currently SDE at GE HealthCare working on healthcare data platfo…
AWS Certified Developer Associate with 2+ years of experience designing and implementing scalable applications, data pipelines, and cloud-native solutions. Currently SDE at GE HealthCare working on healthcare data platforms, RAG-based AI services, containerized microservices and DevOps automation with Terraform/AWS CDK. Proficient in Python, FastAPI, CI/CD, Docker, AWS, Databricks, LLM, and LangChain.
<p>Architected and developed multiple microservices for healthcare data processing, feedback management, and real-time notifications using FastAPI, serving cross-functional teams<br>
Built QuickSight dashboards for log analysis and daily active user tracking<br>
Deployed cloud infrastructure using Terraform across multiple environments<br>
Implemented end-to-end VectorDB deployments with GitLab CI/CD<br>
Developed AWS CDK POC with Clickstream analytics<br>
Built CI/CD pipelines for automated Docker image delivery to ECR<br>
Led TB-scale data migration from legacy healthcare data lake to next-generation platform on Databricks</p>
Architected and developed multiple microservices for healthcare data processing, feedback management, and real-time notifications using FastAPI, serving cross-functional teams, Built QuickSight dashboards for log analysis and daily active user tracking, Deployed cloud infrastructure using Terraform across multiple environments; implemented end-to-end VectorDB deployments with GitLab CI/CD, Developed AWS CDK POC with Clickstream analytics; built CI/CD pipelines for automated Docker image delivery to ECR, Led TB-scale data migration from legacy healthcare data lake to next-generation platform on Databricks
<p>Led PoC for AI-WorkBench data management, deploying MinIO for secure local storage with data sovereignty compliance<br>
Orchestrated Metaflow-based PoC for on-premise data transformation pipeline with modality categorization and thumbnail generation<br>
Pioneered vector embeddings from HL7 non-structured fields within FHIR framework for enhanced healthcare analytics<br>
Implemented scalable VectorDB infrastructure using Terraform<br>
Developed SDK for ingestion, embeddings, and semantic search</p>
Led PoC for AI-WorkBench data management, deploying MinIO for secure local storage with data sovereignty compliance, Orchestrated Metaflow-based PoC for on-premise data transformation pipeline with modality categorization and thumbnail generation, Pioneered vector embeddings from HL7 non-structured fields within FHIR framework for enhanced healthcare analytics, Implemented scalable VectorDB infrastructure using Terraform; developed SDK for ingestion, embeddings, and semantic search
<p>Improved image classification accuracy by 15% using SageMaker<br>
Reduced latency by 50% with CloudFront CDN<br>
Developed chatbot with Amazon Lex and Lambda for appointment scheduling<br>
Utilized Rekognition for facial recognition</p>
Improved image classification accuracy by 15% using SageMaker; reduced latency by 50% with CloudFront CDN, Developed chatbot with Amazon Lex and Lambda for appointment scheduling; utilized Rekognition for facial recognition
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