Karthik Mani
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
Principal ML Engineer
I’m an AI/ML systems engineer with 13+ years of experience building and scaling enterprise software and machine learning products. At Oracle, I am currently an engineer for a multi-source, multi-tenant AI Concierge platform that uses LLMs, vector retrieval, and graph algorithms to automate warehouse extension, subject area creation and feature enablement from natural language. Over the years, I’ve taken products from concept and prototyping to production for large customer bases, with end-to-end ownership across architecture, delivery, reliability, and scale. Outside core work, I’m upskilling in robotics/autonomy stack (world models, ROS2, perception, planning, kinematics) as a long-term technical focus.
Udacity
C++ Nanodegree
January 1, 2019 – Present
Udacity
Self Driving Car Engineer Nanodegree, Autonomous Vehicles
January 1, 2019 – Present
Udacity
Secure and Private AI
January 1, 2019 – January 1, 2019
Illinois Institute of Technology
MS, Computer Science, Specialization in Computational Intelligence
January 1, 2015 – January 1, 2016
Visvesvaraya Technological University
Bachelor of Engineering (BE), Computer Science
January 1, 2007 – January 1, 2011
Oracle
Engineer
November 1, 2019 – Present
Bengaluru, Karnataka, India
Accenture AI Labs
AI Specialist
October 1, 2017 – May 1, 2019
ClearAccessIP
Data Scientist
August 1, 2017 – September 1, 2017
Palo Alto, California, United States
Cisco
Data Scientist, Deep Learning and Computer Vision
March 1, 2017 – July 1, 2017
San Jose, California, United States
Illinois Institute of Technology
Research and Graduate Assistant
May 1, 2015 – December 1, 2016
Chicago, Illinois, United States
Oracle
Software Engineer
September 1, 2011 – December 1, 2014
Bengaluru Area, India
Behavioral Cloning for self driving cars
July 1, 2019 – August 1, 2019
Cloned the behavior of a human driver onto a deep convolutional network using transfer learning inorder to successfully drive in a simulated track
Sensor Fusion - Vehicle Tracking using Extended Kalman Filters
July 1, 2019 – August 1, 2019
-Implemented an Extended Kalman Filter in C++ to estimate the 2d positions and velocities of a moving car. -Fused data from multiple sources such as LIDAR and RADAR
Advanced Lane Detector
June 1, 2019 – July 1, 2019
Lane detector using thresholding, perspective transformation and polynomial fitting techniques.
Garbage Collector for C++
May 1, 2019 – June 1, 2019
Designed and Implemented a Garbage Collector for dynamically allocated memory in C++
OpenStreetMap Route Planner
May 1, 2019 – Present
Implemented Routing Algorithms on OpenStreetMap data in C++
System Monitor
May 1, 2019 – June 1, 2019
Implemented an htop like System Monitor for linux in C++
Traffic Signs Classifier
February 1, 2017 – March 1, 2017
Implemented a deep convolutional neural net (CNN) model to classify traffic signs for self-driving cars. Achieved an accuracy of 94% on test set.
Game playing AI - Isolation
January 1, 2017 – March 1, 2017
Implemented an agent that plays the Game of Isolation using search techniques such as Mini-max, Alpha-Beta pruning and Iterative Deepening
Viterbi Algorithm for Hidden Markov Models
January 1, 2016 – March 1, 2016
Implemented the Viterbi Algorithm for Hidden Markov Models (HMM), and did Parts of Speech (POS) tagging.
Machine Learning Algorithms Implementation
January 1, 2016 – April 1, 2016
Implemented Linear and Logistic Regression, SVM, Naïve bayes and Neural Nets as part of the course CS.584
Text Summarization using Regression models
January 1, 2016 – April 1, 2016
We develop models and extract relevant features for automatic text summarization and investigate the performance of different models on the DUC 2001 dataset. Two different models were developed, one being a ridge regressor and the other one was a multi-layer perceptron. The hyperparameters were varied and their performance were noted. We segregated the summarization task into 2 main steps, the first being sentence ranking and the second step being sentence selection. In the first step, given a document, we sort the sentences based on their Importance, and in the second step, in order to obtain non-redundant sentences, we weed out the sentences that are have high similarity with the previously selected sentences. Achieved a ROGUE-2 score of 0.23 on the generated summaries.
Clustering of Twitter profiles
January 1, 2016 – March 1, 2016
Implemented k-means clustering algorithm to cluster together a sample of user descriptions from Twitter.
Sentiment Analyzer for Movie Reviews
August 1, 2015 – October 1, 2015
Implemented a Text Classifier to categorize the movie reviews into either positive or negative. Inspected coefficients to understand top errors and fiddled around with unigram/ bigram features.
Community Detection and Friends recommendation for Facebook Data
February 1, 2015 – April 1, 2015
Implemented the Girvan Newman algorithm to find communities in Facebook, and recommended friends based on Jaccard coefficients.
Ranking of Wikipedia Articles using PageRank
January 1, 2015 – March 1, 2015
Scraped webpages from Wikipedia and ranked them by implementing the PageRank Algorithm
Database Engine
January 1, 2015 – April 1, 2015
Implemented a miniature version of a database engine such Oracle/ MySQL from scratch in C. It had the following modules: Storage Manager: A storage manager that allows read/writing of blocks to/from a file on disk. Buffer Manager: A buffer manager that manages a buffer of blocks in memory including reading/flushing to disk and block replacement (flushing blocks to disk to make space for reading new blocks from disk). Record Manager: A simple record manager that allows navigation through records, and inserting and deleting records
Game Playing AI - Pacman
January 1, 2015 – March 1, 2015
Search: Implemented depth-first, breadth-first, uniform cost, and A* search algorithms. These algorithms are used to solve navigation and traveling salesman problems in the Pacman world. Multi-Agent Search: Classic Pacman is modeled as both an adversarial and a stochastic search problem. Implemented multiagent minimax and expectimax algorithms, as well as designed evaluation functions.
Deep Learning Specialization
DeepLearning.AI
June 24, 2026 – Present
Cultural Fit Analysis
The candidate exhibits a strong cultural fit for an innovative and technically challenging environment, particularly in ML/AI. The breadth of personal projects, ranging from core computer science concepts to advanced AI applications, demonstrates intellectual curiosity and a passion for the field. Experience in both large corporations (Oracle, Cisco, Accenture) and academic research (IIT) suggests adaptability to different work cultures. The focus on self-driving cars and AI applications aligns well with forward-thinking organizations.
Soft Skills & Operational Fit
The candidate's project descriptions and professional experience suggest a strong problem-solving aptitude and a proactive approach to learning and implementation. The lead engineer role at Oracle indicates leadership potential and ability to drive complex AI initiatives. The diverse range of personal projects also highlights self-motivation and a continuous learning mindset.