Jobin Thomas, MBA

Technical Infrastructure Development and Integration: Spearheaded the implementation of Google Cloud architecture at Mealify, optimizing scalability and performance of the platform. Successfully integrated cloud services, enhancing data storage and processing capabilities. 1. Collaborated closely with cross-functional teams to ensure smooth deployment, resulting in a robust and agile infrastructure that supported the company's growth trajectory. 2. Responsible for Advanced AI API Integration and Security Enhancement. 3. Played a pivotal role in harnessing the power of AI by working with the OpenAI API Integration to develop innovative solutions that enriched user experience on the Mealify platform. 4. Fortified the platform's security by designing and implementing robust registration and login systems. These enhancements not only bolstered user trust but also contributed to a 10% increase in user engagement. 5. Authored comprehensive user manuals detailing the functionality and features of the Google Cloud-integrated architecture, OpenAI API integration, and security measures. 6. Translated complex technical concepts into user-friendly language, enabling both technical and non-technical users to effectively navigate and utilize the platform. This effort led to a marked reduction in user inquiries, freeing up time for the technical team to focus on strategic initiatives.

1. Worked on machine learning, NLP, AWS, ElasticSearch/Opensearch. 2. Helped create ETL for Global Hydrogen Index with ElasticSearch. 3. Utilized Bert for NLP deep learning, graph attention networks, transformers, LSTM. 4. Tensorflow Serving, Machine Learning DevOps, Cloud Engineering DevOps. 5. Data Analytics and Workflow Automation: Proficient in utilizing Alteryx to streamline and enhance data analytics processes. Designed and implemented complex workflows that integrated data from various sources, transformed and cleaned data, performed advanced analytics, and generated actionable insights. Reduced processing time by 15% and improved data accuracy, resulting in more informed decision-making for the organization. 6. Trained employees debugged breaks of ETL pipeline and worked with sales team in creating index.

1. Built Generative Adversarial model for anomaly detection. 2. Built Ganomaly model for anomaly detection. 3. Built Autoencoder for anomaly detection. 4. Process Optimization and Efficiency: Leveraged Alteryx to optimize existing data processes, resulting in significant time and resource savings. Created automated workflows that reduced manual data entry by X hours per week, increasing team efficiency and minimizing errors. Successfully trained and onboarded team members in Alteryx, enabling the department to effectively manage data tasks and achieve consistent results. 5. Built Vanilla Artificial Neural Network for anomaly detection. 6. Built Gaussian Mixture Model for anomaly detection. 7. Automated hyperparameter testing with itertools (pipeline can test 100s models in one go).

1. Worked on and near test and production servers regarding newly installed hbase, hive, Hadoop, Spark. 2. Developed architecture and completed building with team for a high quality and Google tested, ai Citi cards recommender microservice. 3. Educated team on AI and machine learning; Conducted Sprint agile methodology for AI project and acted as scrum master. 4. Trained and Developed Google's wide and deep recommender using tf.estimator; Exported and used tf inference serialized model in flask server. 5. Bootstrapped Flask server microservice for credit card recommendation service. 6. Made TensorFlow model production ready: successful inference calls on protocol buffer TensorFlow using bytelist, tf.feature, tf.features and tf.example.

Handle multiple machine learning engineering projects for clients with pythonic libraries and present it as a solution for their problem. This is an exciting start up and we are scaling up. Let a deep reinforcement learning, deep learning, or machine learning application (web, apache spark) create/improve your business. POS image recognition Project: Built object oriented concepts providing independent convolutional neural network for each waiter's handwriting classification (80% accuracy on training set of 500 custom built images) with intention of transfer learning Stock Market Pattern Classification: Built Angular)S frontend, where expert trader classifies patterns in the market, building training set. Also built recurrent Neural Network for the training sequence classification in django backend Built backend environment where frontend HTTP requests were obtained via django framework which also held machine learning technologies such as tensorflow, sklearn, pandas etc Created frontend dashboards using AngularJS, plotting with d3,js and large dataset for frontend filtering and display, using crossfilter.js

1. Used Sci-kit and Freesurfer Algorithms in progress: general linear models, linear SVM classifier, RBF SVF classifier, Random Forests, extra trees. 2. Used Machine learning approaches statistically classifying brain imaging data from MEG/EEG, functional, diffusion, perfusion, metabolic MRI data with other clinical data. 3. Used Freesurfer, Matlab, shell scripting, Python, scikit-learn, BioHPC to find results working in a large Red Hat cluster applying machine learning on neuroscience and clinical data for statistical analysis on over 500 new subjects. 4. Developed statistical, machine learning approaches to extract radiological imaging (MEG/EEG, functional, diffusion, perfusion, metabolic MRI) and imaging-genomic biomarkers that engender personalized diagnostics and prognostics in clinical neuroscience and oncological applications.

1. Presented persuasive speeches to 80+ people 2. Represented university in multiple events outside campus 3. Worked with others in a team environment to accomplish goals and manage inventory for events 4. Met with prospective students who are interested in engineering