- Leveraging Python and RESTful APIs within the LangChain framework, I spearheaded the design and implementation of LLM-powered chatbots that utilized the Retrieval Augmented Generation (RAG) methodology. This integration with AWS and Azure infrastructure yielded a remarkable 36.45% improvement in automated ticket resolutions. - Working within an Agile methodology and utilizing Git for version control, led the integration efforts for implementing a scalable REST API within TNT (Take-Next-Ticket), a proprietary ticket management platform developed under the aegis of JP Morgan, as evidenced by the associated patent (Patent Link). This initiative expedited ticket routing and resolution processes.- Utilizing Python, Flask, and REST APIs, pioneered the development of scalable microservices dedicated to internal link routing. The implementation of the Consistent Hashing Cyclic Algorithm within this project resulted in a notable 16% reduction in fault detection time, accompanied by a streamlined time complexity for issue resolution. - Through collaboration with the JPMC ML Center, I orchestrated the deployment of diverse BERT-based REST APIs through the sophisticated ML Transformers pipelines, thereby augmenting the platform's machine learning capabilities.- Harnessing the synergies of Docker and AWS Elastic Kubernetes Service (EKS), I engineered and automated Containerization pipelines for application deployment, ensuring seamless scalability and efficiency. - Leveraging Terraform, part of Infrastructure Management endeavors, ensuring the robustness and scalability of the underlying architecture. - Mentoring team members, I imparted insights into cloud technologies, architectural best practices, and fostered a culture of solution adoption to drive innovation and efficiency within the team.

- As a member of the JPMC HRS Research and Development (R&D) Team, I actively contributed to all phases of project development, including ideation, research, prototyping, and the migration of both web and machine learning applications. My role within this Agile cross-functional team involved collaboration with colleagues in both India and the USA, utilizing Git for version control. - Spearheading the comprehensive testing initiative for machine learning and REST-based projects using PyTest, I achieved an impressive 97% code coverage. This endeavor required meticulous attention to detail and a deep understanding of test-driven development (TDD) methodologies.- Playing a pivotal role in integrating our product with SONAR, I enhanced its capabilities and ensured its alignment with industry standards. - Tackling a critical deployment challenge, I successfully containerized large-scale machine learning applications onto JPMC's internal Kubernetes service (GKP) and Kube platform. This task demanded a high level of expertise in containerization technologies and understanding of Kubernetes architecture.- In addition to my hands-on contributions, I engaged in research and ideation activities focused on innovative R&D projects, exploring solutions utilizing Min-Max and Genetic Algorithm methodologies. This experience contributed valuable insights to the team's strategic initiatives. - Expanding upon this experience with similar projects involving advanced algorithmic solutions and deployment strategies, I further honed my expertise in the intersection of software development, machine learning, and infrastructure management.

- Architected and built a robust RESTful API leveraging Spring Boot and Spring Data JPA for dynamic query execution, achieving a significant 26% backend efficiency improvement.- Collaborated in the design and implementation of a reusable frontend component for data acquisition, streamlining manual processes and accelerating development velocity. (Unit tested with JUnit 5)

- Implemented secure user authentication APIs leveraging robust cryptographic protocols for user verification.- Integrated API with React frontend, optimizing load times by 12% through performance tuning.