• Proactively identified bottlenecks in internal engineering workflows, then proposed, developed, and deployed end-to-end solutions that improved team efficiency and development velocity.

• Built a generalized file processing system leveraging vLLM, capable of extracting structured data from highly unstructured clinical documents with 99.99%+ accuracy, using a flexible marker-file schema.

• Designed an innovative marker-based learning framework to drastically reduce parsing cost and latency while improving extraction accuracy, setting new internal standards for document understanding.

• Developed an internal agentic coding assistant that automated repetitive workflows, saving over 80% of engineering time and significantly accelerating customer onboarding.

• Built LiveQ, an AI-powered desktop assistant that helps users in real time through voice-based interaction and context-aware intelligence.

• Designed a multimodal agent framework capable of interpreting on-screen context, user intent, and personalized workflows to deliver seamless, adaptive assistance.

• Developed the web console using Next.js and the main application using Electron, integrating both into a cohesive user experience.

• Leveraged LiveKit as the core voice infrastructure, and developed a custom in-house AI agent system optimized for latency, reliability, and dynamic task routing.

• Personally conducted 50+ client interviews within the first month to gather feedback, validate use cases, and accelerate the development cycle through rapid iteration.

• Built complete video media processing pipelines from scratch.

• Utilized AWS s3, AWS Textract, AWS CloudFront, AWS LightSail, and MySQL database to construct complete video processing and evaluation pipelines.

• Used OOP and Multi-threading to improve the performance of data pipelines.

• Individually developed a video ranking system that displays the most appropriate content to the end users.

• Individually Constructed an administration panel web application using ant Design Pro, Typescript, and Flask to manage all application users' data.

• Developed a conversational agent powered by a Large Language Model (LLM) to assist customers in completing tasks requiring multiple steps and decisions while incorporate multimodal (voice and vision) customer experiences. Try it! Say "Alexa, lets work together"

• The application was developed using Amazon's ASK API and deployed on Amazon's EC2 instances for echo devices serving a user base of >50 million, I designed the agents backend using a microservice architecture. Implementing containerization (GPU Docker), caching, load balancing(ECS) for efficient performance and auto-scaling.

• Designed and implemented a hybrid system to align LLM-based responses with AWS safety standards, reducing conversation friction and enhancing user response rates by 30%.

• Analysed log data to identify & fix bugs with backend services, communicated findings to engineers at Amazon