Shranith Linga
ML EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Machine Learning and Engineering at Apple | SMIEEE
Shranith is a seasoned Machine Learning Engineer with expertise in rapid prototyping, productionizing, and deploying ML-based cloud and edge applications on a large scale. He possesses a comprehensive understanding of machine learning algorithms, ranging from classical shallow approaches to deep neural networks. During his tenure at IBM Watson, Shranith developed a Natural Language Processing (NLP) engine that handled approximately 15 million real-time API requests daily, performing tasks such as Key Phrase Extraction, Named Entity Recognition, and Document Classification across 12 languages. At Scout, Amazon's autonomous delivery robot initiative, Shranith was instrumental in constructing core machine learning pipelines, encompassing data sampling, annotation, model training, and evaluation. This work aimed to enhance perception capabilities by integrating input from multiple sensors. Shranith is currently focused on refining Apple's media product services, with the objective of directing his skills towards improving human interaction with technology. Skill Set: Programming Languages: C++, Python, Java Tools: PyTorch, TensorFlow, MxNet, Caffe, NLTK, OpenCV, Git Operating Systems: Linux/Ubuntu, MacOS Big Data Technologies: Hadoop, Spark, Hive, Kafka Cloud Technologies: AWS, Docker, Kubernetes
Arizona State University
Master’s Degree, Computer Science
January 1, 2015 – January 1, 2016
ABV-Indian Institute of Information Technology and Management
Bachelor’s Degree, Information Technology
January 1, 2010 – January 1, 2015
Packt
Technical Reviewer
November 1, 2023 – Present
Apple
Machine Learning Technical Lead
October 1, 2022 – Present
Seattle, WA · On-site
Amazon
Machine Learning Engineer
January 1, 2020 – October 1, 2022
Seattle, Washington, United States · On-site
IBM
Advisory Machine Learning Engineer
January 1, 2019 – January 1, 2020
Denver, Colorado, United States
IBM
Staff Machine Learning Engineer
January 1, 2017 – January 1, 2019
Denver, Colorado, United States
IBM
Cognitive Software Developer Intern
June 1, 2016 – August 1, 2016
Denver, Colorado, United States
A Visual Recommend-er for the Stack overflow - REP. Boost
October 1, 2016 – Present
Developed a Visual Recommend-er for the stack overflow with a motivation to help a user to gain the reputation quickly based on his own preferences Developed a web based Recommend-er which provides a tunable preferences for the recommendation
Gaussian Process Regression
September 1, 2016 – Present
Implemented Gaussian Process Regression from scratch. Regressed the values for a real world Data set, with an RMSE of 0.6467
Airbnb New User Bookings
September 1, 2016 – Present
Predict the country where a new user would book based on the demographic data. Implemented Auto Encoder for filling in the missing values in the Dataset Implemented Boosting for predicting the user booking by assuming the decision trees as week classifiers
Parts of Speech Tagger
September 1, 2015 – Present
System tags the parts of speech tags like Noun, Verb, Adverb etc to a given sentence with 93.6% accuracy.It is based on Hidden Markov Model algorithm and developed in Python
Python compiler (mini) based on C++.
September 1, 2015 – November 1, 2015
A small size compiler for a Python type language supports basic data types of INT, STRING, BOOLEAN and logical operations if else, for, while, do while.Gives compile time errors and output of the program.Based on C++.
Question Answering System Based on Remedia Corpus
August 1, 2015 – November 1, 2015
Natural Language processing system based on Kparser (http://bioai8core.fulton.asu.edu/kparser) to answer questions of type Who, Where, When, Why, What for a comprehensive passage. The state of art system accuracy is 66.7 and our system when evaluated generated an accuracy of 60.7% in various levels of difficulties.
Name Entity Recognizer for Informal texts
August 1, 2014 – Present
Name Entity Recognition for the whatsapp chats. It is used to recognize the entities from the text.Modelled the chats and preprocessed the chats for the spelling corrections. Support Vector machine based classification of the tagset to identify different entites.
Review Analyzer
March 1, 2014 – Present
Analyzing the individual reviews for Moto G from Flipkart website, aggregating the reviews for all phone features like battery, touch, processing power, camera from the individual reviews. Final rating for all the features of the phone.
Recruitment Rules
December 1, 2012 – Present
Designed and developed the application backend, front end.
Online Detailed Application Form
January 1, 2012 – Present
Worked to improve the Detailed Online Application Form (DAF) project of Union Public Service Commission (UPSC).
IBM AI Skills Academy Coach
IBM
June 24, 2026 – Present
IBM Blockchain Essentials
IBM
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
IBM MENTOR
IBM
June 24, 2026 – Present
Generative AI Fundamentals
Databricks
June 24, 2026 – Present
Deep Learning Prerequisites
Udemy
June 24, 2026 – Present
Deep Learning with PyTorch : GradCAM
Coursera
June 24, 2026 – Present
Fine Tune BERT for Text Classification with TensorFlow
Coursera
June 24, 2026 – Present
PyTorch: Techniques and Ecosystem Tools
DeepLearning.AI
June 24, 2026 – Present
PyTorch: Fundamentals
DeepLearning.AI
June 24, 2026 – Present
Deep Learning with PyTorch : Object Localization
Coursera
June 24, 2026 – Present
Introduction to Big Data with Spark and Hadoop
Coursera
June 24, 2026 – Present
LlamaIndex- Develop LLM powered applications with LlamaIndex
Udemy
June 24, 2026 – Present
IBM Developer Jump Start
IBM
June 24, 2026 – Present
Computer Vision - Object Detection with OpenCV and Python
Coursera
June 24, 2026 – Present
Deep Learning with PyTorch : Image Segmentation
Coursera
June 24, 2026 – Present
Data Analysis Using Pyspark
Coursera
June 24, 2026 – Present
Generative AI with Large Language Models
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from academic implementations to large-scale enterprise solutions at Apple, Amazon, and IBM, indicates adaptability and a broad interest in various ML applications. Their involvement in IBM's AI Academy Initiative and mentorship roles suggests a willingness to contribute to team growth and knowledge sharing. The target role of ML Engineer aligns well with their extensive professional experience and project focus.
Soft Skills & Operational Fit
The candidate's experience as a Technical Lead and mentor at IBM suggests strong leadership, communication, and collaboration skills. Their role as a Technical Reviewer at Packt further indicates attention to detail and critical evaluation abilities. The project descriptions, while lacking specific technologies, demonstrate a proactive and problem-solving mindset.