• Front-end development for React and Next.js apps, making interfaces for doctors that use live data, helping with workflows with AI, and letting users create interactive documents. Made UI components that are responsive and easy to use while making sure they work well on the client side.

• Created backend systems for clinician-oriented apps that include EHR and workflow systems to make data management easier, reduce cognitive overload, help with quick clinical decision-making, and use WebSocket-based notifications to implement GraphQL APIs.

• Used pgvector to create and improve PostgreSQL data structures to make sure that AI, workflow, and documentation systems can store, access, and share data correctly.

• OpenAI, Ollama, and in-database search capabilities helped doctors get accurate answers based on live patients' information through AI-based insights, notes, STT/TTS, and RAG pipelines.

• To bring together patient information, cut down on the costs of administrative work, and make sure clinics ran smoothly, they used integration with EHR and workflow solutions and made backend services.

• Flask and FastAPI were used to make Python services, and dockerized containers were used to make APIs for AI functions, documentation, and connecting clinical workflows.

• Using event-driven architecture, OCR-based ingestion pipelines were made to automate the extraction of EMR data. This saved time on paperwork and helped doctors work with patients more quickly.

• The MCP server was made to process clinical data, which helped AI make reports using models like ChatGPT-5.2, Gemini, and Ollama.

• Built and designed scalable back-end systems for a real estate management system using Python and Django. Built microservices to handle more than 10,000 users per month and added real-time messaging using the WebSocket protocol.

• Used Django to make REST and GraphQL APIs that could handle a lot of traffic. Used testing frameworks like pytest to make the APIs much more responsive, cutting their latency by about 25% (from 800ms to 600ms).

• Used ECS, EKS, Fargate, EC2, and S3 to set up and manage AWS cloud infrastructure. Used Terraform to automate infrastructure setup, which cut setup times by 40%.

• I set up a CI/CD pipeline in AWS CloudFormation and AWS CodePipeline using GitHub Actions. This helped me improve my DevOps practices by automating testing and deployments better, which led to faster weekly releases.

• Created log reports for monitoring with the AWS CloudWatch service and added Node.js services to them.

• I changed the Figma design from pixels to make sure that the UI of the investment app works well on both web and non-static devices using React JS and JavaScript.

• To make sure that the interface transitions are smooth, work closely with the design team. Prototypes were made to test the UX design. Because of this, it is less likely that there will be a need to stay redesigned in the future.

• I made Charts and Backend APIs that let people see facts in a way that helped them understand financial facts.

• The test and code reviews that were done with the right salvage for 20% of the work above the front stop and 25% of the interface errors.

• Collaborated with CTO to develop and release new web and mobile features based on React and GraphQL, allowing customers to view their account details via mobile applications.

• Participated in developing front-end and back-end systems for microservices through React, GraphQL, and Git technology stacks; tested developed code by implementing unit and integration tests using pytest.

• Collaborated with the product and design team members to implement UI components with React, allowing users to experience smooth onboarding and increased satisfaction with their use.

• Analyzed performance issues through MySQL slow query log and performed optimizations in databases and caching; collaborated to provide API integration with other platforms.