Led the AppDynamics Python Agent team, elevating the product's adaptability by engineering compatibility across diverse operating systems and integrating instrumentation for Python libraries like aiohttp and FastAPI, driving enhanced monitoring capabilities.Pioneered the development of the AppDynamics Agents Fleet Management system, enabling customers to manage their agents directly from the AppD Controller UI, which streamlined operational workflows and boosted production efficiency by 50%.Contributed to the robustness of the AppDynamics CSaaS Controller by resolving high-priority customer bugs, playing a key role in fortifying the reliability and performance of the controller infrastructure.Secured consecutive victories in the annual in-house hackathon for two years, with innovative ideas that were subsequently implemented into AppDynamics CSaaS and Agents, demonstrating a strong capacity for impactful innovation.Led a crucial scalability migration of the language agent by introducing a new Go proxy package as a mediator between AppD agents and the AppD controller, resulting in a 90% reduction in memory consumption in the customer environment.Led a strategic migration of Fleet Management code from Ansible to Golang, resulting in an 80% increase in operational efficiency and significantly reducing incidences of crashes and timeouts, thereby enhancing system stability.Actively engaged in the system design of new features slated for present to future releases, contributing valuable insights and technical expertise to ensure scalability, performance and long-term product visionInnovated a monitoring solution for the OpenAI Python SDK by developing metrics to track and analyze cost and usage patterns of customer prompts, enabling data-driven optimization for AI-powered applications as outlined in Cisco AppDynamics Blog.Tech Stack Used: - Python, Java, Golang ,Docker, JavaScript, Jenkins, RDS, Hibernate, SQL, Golang, Ansible, AWS Services

1. Built Accuracy Computing Engine for our Automation System which will compute accuracy of every pdf's as per edits by client which will help to find low-level extractions, thus decreasing the work load of Data ops by 90%.Tools:- Python , SQLAlchemy, PostgresSQL, AWS cloudwatch, AWS Lambda2. Built mailing system for our system which generates accuracy report from AWS Lambda and sends it to the stakeholders.Techstack:- AWS SES, AWS Lambda , Python2.Building UI Dashboard and automation system for OCR extraction and auto population of data in the respective fields with the help of Reactive forms . Tools & framework:- Angular 8 , Django, postgres ,Aws S3 buckets3. Worked on CD pipeline for dashboard deployment using bones , NAWS & AWS services.4. Worked on Model Training and analysing the data if the data modelling is done correctly by using Soroco internal products, writing python and YAML scripts for OCR extraction . Tools:- Python, AWS EC2, Aws S3

Working on MEAN stack application helping to grow a product from scratch, building optimised algorithm for backend services.Tools & framework:- VSCode, Laravel , Node js, LAMP , Angular 8 , MongoDB

Worked as a backend developer in MERN stack /Node js with mongo projects as well as elastic search and data scrapping