Sri K. Nellutla

Former Technical Product Manager Intern @ | Software Engineering Student @



Results-driven software engineering student with proven experience building production-quality applications in AI (factory automation and machine learning for energy demand prediction). Currently creating software security products using machine learning as a TPM intern at BlackBerry.


Sri enjoys reading books related to corporate strategy, entrepreneurship, and organizational psychology. Some of his favorite books include Originals by Adam Grant, Good to Great by Jim Collins, and Smarter Faster Better by Charles Duhigg. Other than reading, Sri loves travelling, camping, and attending hackathons.



Technical Product Manager Intern, BlackBerry

September - December

BlackBerry is a multinational company specializing in enterprise software and IOT. BlackBerry inherited strong roots in ML-based preventative endpoint security through the acquisition of Cylance.

Key Achievements:
• Conducted pricing analysis, and defined channel strategy as part of go-to-market for a new security product with a $700M target addressable market (TAM)
• Owned the UX front for the security product’s brand-new iOS and Android apps; created UX wireframes in Adobe XD, then worked with the design team to create mockups
• Presented high-level architecture diagrams to executives accounting for trade-offs and risks with different technical decisions, then worked with the development team to create feature specs and user stories (JIRA/Confluence)
• Proposed and pitched product ideas to strengthen our partnership initiatives with multinational companies

Machine Learning Engineering Intern, EnPowered

January - April

Energy demand prediction start-up that saves commercial facilities millions of dollars during high-peak days.

Key Achievements:
• Introduced new real-time prediction model; increased accuracy by 5%, and significantly decreased false negatives by implementing custom loss functions in TensorFlow
• Decreased mean percent error by 1.1% by applying multiple techniques (SMOTE-for-Regression, Dropout, Locally Weighted Learning) from 100+ research papers to improve existing regression models
• Redesigned enterprise dashboard data visualization using Plotly, D3/Highcharts – created better customer experience for high-fidelity, real-time predictions
• Migrated the entire training data to AWS – wrote a script to monitor a Hadoop cluster on AWS EMR using Boto3, and executed Hive query templates against AWS S3


Sofware Developer Intern, CareGo Tek

May - August

Company’s product is a patented warehouse automation system; from material production to truck-loading.

Key Achievements:
Optimized and refactored warehouse decision logic; increased the throughput of steel production jobs by over 40%, currently used in production more than 100 times daily.