Kalyan Nelluri

• Spearheading the Treasury Technology team, which is dedicated to develop advanced tools for optimizing core treasury functions for Hedge Funds.
• Collaborated closely with Front Office Trading groups and provided various tools to optimize opportunity cost involved causing direct revenue impact in the order of several millions of dollars.
• Successfully scaled the team from 4 to over 12 developers, seeded 2 internal teams across 3 different sites (Hyderabad, Bangalore, Gurugram) to solve complex niche challenges in CRM(Counter Party Relationship Management) and Treasury.
• Oversaw annual budget planning processes, ensuring the planning of strategic and tactical objectives of the group.

• Lead Data Intelligence(DI) and Experimentation Backend and Mobile teams in Uber
• DI team is responsible for building various libraries that make logging reliable on Mobile and standardize logging in backend services
• Experimentation team is responsible for developing framework within mobile for building either feature flags or A/B tests
• Seeded experimentation team in India and grew it to a size of 5 developers.

• Responsible for development and delivery of software projects with strong focus on quality and timelines
• Manage a team with a broad range of responsibilities which include client interactions, requirements gathering, design, development, unit testing, code reviews and technical documentation
• Supervising and collaborating with distributed product development teams globally.

• Migrated Margin Computation Framework: Upgraded an existing margin computation framework to a multi-threaded architecture, incorporating advanced caching mechanisms to store reference data. This enhancement enabled real-time margin computation, significantly improving processing speed and accuracy.

Technologies Used: Perl, Python, Redis distributed cache on AWS

• Simulation System Development: Designed and developed a simulation system that allows analysts to model real-time trading scenarios across various broker methodologies. This system facilitates position transfers and optimizes margin calculations for portfolios.

Technologies Used: JSP, Spring, WebServices

• Margin Calculator Configuration Framework: Created a framework for exposing and updating margin calculator configurations, enabling users to adjust parameters and instantly reflect changes in margin calculations.

Technologies Used: Java, Spring

• Projects Worked: Cairo: Cairo is a general purpose image processing and recognition system capable of extracting text out of digital images
• Technologies: Image Processing using OpenCV, Tesseract
• Quiddity: System which attempts to identify seller pain points while selling with amazon by tracking their behavior across various pages and provide self- help solutions
• Big Data Technologies: Hadoop, Map Reduce, Apache Spark, Hive
• Feedback Removal: We have designed an automated way to classify whether feedbacks are valid or not for sellers
• We have used text classification algorithms and regex based matching in this project
• Machine Learning Algorithms: Naïve Bayes
• SPOT: Developed a unified framework, which acts as a platform for the associates to resolve the contacts from the sellers
• This platform contains diagnostics tools for associates to identify the seller facing issue and tools for fixing these issues
• Project 911: Improve the seller experience for premium sellers by creating custom routing mechanism, which prioritizes their contacts by routing to special queues with improved service level times
• IIDP Automation: Created a backend robotic system, which makes a contact go through a rules engine and accordingly automates the contact without associate intervention.

• News Search: Buzz serves as news search over thousands of news sources in real time
• This system has 3 main parts: Crawler: We crawl 1000+ hub pages from 300+ sources
• This includes RSS and HTML pages
• The module fetches the news feeds in the configured interval and stores the content in the data-store
• Technologies used: Core Java, JDBC, MySQL Parser: The feeds downloaded by crawler are parsed in batches and the parsed content is stored in the data-store.HTML feeds are parsed using custom HTML parsers.RSS feeds are parsed using feedparser
• Technologies used: Python, python-MYSQLDB, Parser, FeedParser, and MYSQL Indexer: Pushes the parsed data to SOLR for indexing
• It is responsible for indexing the parsed data
• Technologies used: Solr as indexer customized according to our requirement.

GPA: 9.06

Marks: 93

Marks: 81.1