[Working Remotely]

.NET Core Migration - Global Capital Markets Investments Portal.

• Working on migrating a project from .NET Framework 4.8 to .NET Core 8 to reduce costs associated with Windows containers by migrating to Linux Containers.

• Tech: Apache Kafka, Spark, Airflow, Kubernetes, Docker, Snowflake, Redshift, Lambda, EKS,

Amazon Web Services, Spring Boot, Java, Python.

• Successfully transitioned Jenkins operations to Kubernetes by meticulously re-engineering the Operations Console's Jenkins scripts using Java. Integrated these scripts into a Workload executor within a Kubernetes framework, enhancing operational efficiency.

• Engineered end-to-end data pipelines, harnessing GitHub, Snowflake, and New Relic as dynamic data sources. Leveraged the internal Adapter Framework to optimize data extraction and loading processes, augmenting business functionalities.

• Orchestrated the streamlined construction and deployment of AWS Lambda functions, pivotal in enabling email alerts and event logging from Apache Airflow. Conducted an exhaustive evaluation of development and deployment strategies, implementing a robust protocol that employed Docker and AWS S3. Automated the deployment process using Jenkins, ensuring seamless functionality.

• Revamped the Application Control Plane, automating diverse database functions within the customer Single Sign-On ecosystem. Elevated user experience by introducing a range of tailored features for distinct customer categories and accounts, enriching their access to a diverse suite of applications.

Software Engineer. Worked with Angular, Express, NodeJS, RxJS, ngx-bootstrap, prime-ng.

◦ Technologies: NLTK library, Python, PyTorch, Pandas, Matplotlib, Numpy.

◦ Worked with multiple teams to gather real-time banking data and helped organize them to analyze them using Natural Language Processing techniques with the NLTK library for Named-Entity-Recognition on the extracted data.

Mentor: Dr Jaleel UC (Principal Scientist), OSPF Drug Discovery Lab, NIAS, IISc.

• Tech Stack: R

• Engaged in the application of Unsupervised Deep Learning methods like Kohonen Self-Organizing Maps to decipher intricate relationships among diagnostic parameters within a medical dataset.

• Specialized in Kohonen self-organizing maps, a neural network architecture renowned for competitive learning, enabling the unsupervised clustering of data.

• Acquired proficiency in Vector Quantization and other competitive learning techniques, pertinent to the realm of unsupervised learning.

• Employed R as the principal tool to actualize self-organizing Kohonen maps, subsequently visualizing multifaceted components of the derived map.

• Academic Conference Volunteering: Was part of the organizing committee of the United Kingdom India Meet on Emerging Innovations in AMR (Anti-Microbial Research) held on 7th June 2019 at National Institue of Advanced Studies, IISc.