AI/ML DEVELOPER

Ramesh Reddy

PROFESSIONAL SUMMARY:

Extensive 10+ years of experience in design, development and Testing using Software Development Life Cycle. Proficient in developing AI algorithms and models to solve complex business problems.

Demonstrated ability to apply AI techniques to optimize processes and enhance user experiences.

Extensive experience in natural language processing (NLP) and computer vision applications.

Strong knowledge of AI ethics and responsible AI practices.

Expertise in interpreting and fine-tuning models generated by Auto-Sklearn.

Demonstrated the cost-effectiveness of Auto-Sklearn solutions for ML projects.

Expertise in implementing custom layers and loss functions in MXNet.

Experience with Keras Callbacks for model checkpointing and early stopping.

Proficiency in AI programming languages such as Python and TensorFlow.

Expertise in supervised, unsupervised, and semi-supervised machine learning algorithms.

Proficient in feature engineering and data preprocessing to improve model performance.

Proficient in using Auto-Sklearn for automated machine learning model selection and hyperparameter optimization.

Extensive experience with popular CNN architectures such as VGG, ResNet, and Inception.

Proficient in using activation functions, weight initialization techniques, and regularization methods to optimize neural network performance.

Demonstrated the ability to troubleshoot and debug neural network training and deployment issues.

Experience with feature selection and preprocessing capabilities of Auto-Sklearn.

Proficient in using H2O.ai's machine learning and artificial intelligence platform.

Proficient in building and training deep neural networks using the Keras API.

Proficient in using MXNet as a deep learning framework for building neural networks.

Excellent experience in a Python based environment, along with data analytics, data wrangling and Excel data extracts.

Proficient in designing and training CNNs for image classification and object detection tasks.

Expertise in establishing database connections for Python by configuring packages like JDBC, MySQL-Python.

Experienced in working with Python packages like NumPy, Pandas, Beautiful Soup, Scikit-learn, Requests, matplotlib, PyTables, SciPy.

Experienced on automation using the Python Scripting language.

Used a variety of deep learning frameworks including TensorFlow, Java, PyTorch, PyCharm, Keras, JSON web token, FastAI, and many more. Used TDD/BDD with unit testing using Junit, Mockito and Karma.

AutoML tools often handle missing data, duplicate records, and outlier detection automatically.

Build Docker container/Docker Swarm clusters managed by Kubernetes Linux, Bash, GIT, and Docker on Google Cloud Platform (GCP). Used Plotly to create python powered dashboard.

Have implemented pyspark for Transformation and Actions in Apache Spark.

EDUCATION

Bachelor of Science and Engineering from University Texas.

Certified AI/ML Engineer

Technical Skills

AI, Machine Learning, Auto-Sklearn, H2O.ai, Edge AI, TensorFlow, PyTorch, Scikit-learn, Keras, MXNet, Neural Networks, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Transformer Models, Python, BERT, GPT (Generative Pre-trained Transformer), Word Embeddings, Named Entity Recognition (NER), Sentiment Analysis, OpenCV, YOLO, ImageNet, OpenAI Gym, Deep Q-Networks (DQN), Policy Gradients, Pandas, Matplotlib, Seaborn, Tableau, Power BI, AWS, Azure, Google Cloud, SQL, NOSQL, Hadoop, Spark, Docker, Kubernetes, Jenkins, GitLab, CI/CD, LIME, SHAP

PROFESSIONAL EXPERIENCE:

Cardinal health, NY Aug 2022 - Current

Sr. AI/ML Developer

RESPONSIBILITIES:

Designed and maintained AI/ML data pipelines for ongoing model performance monitoring and evaluation.

Developed and integrated natural language processing (NLP) and AI frameworks using Keras and Tensor Flow.

Implemented machine learning models optimized for edge computing and IoT devices, enhancing real-time object detection capabilities.

Leveraged H2O.ai for advanced machine learning tasks, including time series forecasting, anomaly detection, and classification.

Built and deployed REST APIs using Python with Django and Flask, facilitating integration of machine learning models into applications.

Applied back propagation algorithms to train neural networks, optimizing performance with gradient descent methods.

Created scalable web applications with Python, Django, and MySQL, focusing on robust server-side functionality.

Developed custom web crawlers using Python, Scrapy, and BeautifulSoup for efficient data extraction and processing.

Utilized Explainable AI techniques, such as LIME and SHAP, to ensure transparency and fairness in machine learning models.

Managed cloud-based solutions and automated deployment processes using Jenkins in Docker containers, AWS, and GCP.

Employed Dask for large-scale data processing, working with data structures that resemble pandas Data Frames and NumPy arrays.

Implemented advanced machine learning algorithms with Scikit-learn, TensorFlow, and PyTorch for improved data analysis and model accuracy.

Ensured security and privacy in edge AI deployments, addressing considerations related to data protection and bias.

Worked on agile software development methodologies, including requirements gathering, capacity planning, and TDD.

Developed and maintained Python and Java-based web applications, utilizing distributed servers and MySQL database clusters.

Integrated H2O's interpretable machine learning algorithms to provide transparency and understanding of AI model decisions.

Refactored and optimized existing codebases, including converting Java code to Kotlin for improved application performance.

Developed and executed unit test cases using JUnit, Mockito, and PyUnit to ensure the reliability of software components.

Implemented cloud storage solutions with Azure and Kafka for handling large volumes of data and message brokering.

Utilized JavaScript ES6 and TypeScript for front-end development, enhancing user interfaces and application functionality.

Technologies Used: AI, Machine Learning, Auto-Sklearn, H2O.ai, Edge AI, TensorFlow, PyTorch, Scikit-learn, Keras, MXNet, Neural Networks, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Transformer Models, Python, BERT, GPT (Generative Pre-trained Transformer), Word Embeddings, Named Entity Recognition (NER), Sentiment Analysis, OpenCV, YOLO, ImageNet, OpenAI Gym, Deep Q-Networks (DQN), Policy Gradients, Pandas, Matplotlib, Seaborn, Tableau, Power BI, AWS, Azure, Google Cloud, SQL, NOSQL, Hadoop, Spark, Docker, Kubernetes, Jenkins, GitLab, CI/CD, LIME, SHAP

MetLife, Boston, MA June 2020 – July 2022

Sr. AI/ML Developer

RESPONSIBILITIES:

Designed and implemented machine learning algorithms for time series data, including LMS, regression, filtering, and neural networks.

Evaluated various machine learning algorithms such as Support Vector Machines, Random Forest, and XGBoost, determining Decision Trees as the most effective model.

Developed AI and machine learning solutions using Python and R, transitioning prototypes from R to Scala scripts for Spark ML.

Led computer vision projects with Keras, focusing on image classification and object detection, while collaborating with hardware engineers for custom AI accelerators.

Ensured security and privacy in edge AI deployments, enhancing model robustness and safeguarding sensitive data.

Applied data augmentation techniques to improve the generalization of convolutional neural networks (CNNs) for diverse applications.

Addressed edge AI use cases in industries such as healthcare and autonomous vehicles, staying updated on trends like federated learning and on-device training.

Created Python-based monitoring tools and fine-tuned hyperparameters to optimize model performance and accuracy.

Developed Python APIs and Django-based web applications, ensuring compatibility across Linux and Windows environments and maintaining them using SVN.

Leveraged asynchronous capabilities in FAST API to handle high-concurrency scenarios, improving system responsiveness.

Implemented and managed microservices using Docker and Puppet, optimizing deployment and configuration processes.

Used AutoML tools for model cross-validation, ensuring accurate performance assessments and model generalization.

Applied Pandas for statistical analysis and developed machine learning time series solutions with libraries like PyTorch and TensorFlow.

Designed and deployed APIs using frameworks like Flask, Tornado, and Node.js, incorporating RabbitMQ and Celery for asynchronous task distribution.

Utilized Vue.js and Redux SAGA for front-end development, managing state transitions and enhancing UI/UX across web applications.

Integrated AWS services for cloud deployment, including EC2, Lambda, and S3, and used Jenkins for CI/CD pipelines to streamline development workflows.

Developed and maintained web applications with databases like MongoDB and MySQL, performing database migrations with SQLAlchemy and writing PL/SQL stored procedures.

Implemented automated testing frameworks using tools such as pytest, Mocha, and Selenium, ensuring comprehensive unit and integration test coverage.

Managed cloud-based deployments with Terraform and Kubernetes, handling microservice builds, Docker containers, and continuous integration processes.

Collaborated in an Agile/Scrum environment, emphasizing fast-paced deliverables and adopting Test-Driven Development (TDD) and Behavior-Driven Development (BDD) methodologies.

Technologies Used: AI, Machine Learning, H2O.ai, Edge AI, TensorFlow, PyTorch, Scikit-learn, Keras, MXNet, Neural Networks, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Transformer Models, Python, BERT, GPT (Generative Pre-trained Transformer), Word Embeddings, Named Entity Recognition (NER), Sentiment Analysis, OpenCV, YOLO, ImageNet, OpenAI Gym, Deep Q-Networks (DQN), Policy Gradients, Pandas, Matplotlib, Seaborn, Tableau, Power BI, AWS, Azure, Google Cloud, SQL, NOSQL, Hadoop, Spark, Docker, Kubernetes, Jenkins, GitLab, CI/CD

Chevron Corp, Seattle WA Nov 2017 – May 2020

AI/ML Developer

RESPONSIBILITIES:

Enhanced Python/Django modules to output data in specified formats, improving data processing and visualization.

Developed a functional package using Erlang and Python, contributing to diverse application functionalities.

Implemented user interface guidelines and standards throughout the development and maintenance of applications using Python.

Automated cloud deployment processes using Python and AWS CloudFormation templates to streamline setup and configuration.

Utilized Django’s built-in authentication mechanisms to ensure secure login and user access management.

Managed URLs and application parameters through Django’s configuration settings, improving application routing and flexibility.

Engaged in database-driven web application development with frameworks like Django on Python, optimizing application performance.

Successfully migrated the default Django database from SQLite to PostgreSQL to enhance data efficiency, integrity, and security.

Applied iBatis and MyBatis ORM tools to automate the mapping between SQL databases and Java objects, simplifying database interactions.

Used the Pandas API to format data into time series and tabular formats, facilitating efficient timestamp data manipulation and retrieval.

Developed an intranet portal for managing Amazon AWS EC2 servers using Python, Tornado, and MongoDB, improving server management capabilities.

Employed Apache Cordova for cross-platform development, leveraging HTML5, CSS/CSS3, and JavaScript for mobile and desktop applications.

Created single-page applications with AngularJS, binding data to specific views and synchronizing it with the server, while implementing responsive design using Bootstrap.js and Sass.

Adopted a test-driven development approach, implementing unit tests with the Python Unit Test framework and developing web kits and search engines.

Created scenarios and unit test cases for Behavior-Driven Development (BDD) using Selenium and Cucumber to ensure application reliability.

Used GitHub and Git bash for version control, managing code changes and maintaining repository versions in an agile development environment.

Deployed applications on Amazon AWS using EC2, S3, and EBS, and evaluated Chef and Puppet frameworks for automating cloud deployment and operations.

Wrote Python scripts to parse XML documents and load data into databases, and developed performance calculation programs using SQL Alchemy.

Integrated Crucible with Jira and HipChat for code review and team collaboration, enhancing workflow efficiency and communication.

Applied agile methodology for software development and used Atlassian products like Jira, HipChat, and Confluence to support team collaboration and project tracking.

Technologies Used: Python, Django, ORM, pandas, Tornado, JavaScript, HTML5, CSS3, AngularJS, Bootstrap, jQuery, JSON web token, SSO/SAML, Pyramid, Java, Oracle, Rest, Eclipse, Websphere, Git, SVN, unittest, Selenium, Agile, AWS EC2, S3, Dynamo DB, Maven, JSON, XML, Jira, Linux, Hipchat, Jenkins

Liberty Mutual, Columbus, OH September 2015 – Oct 2017

Python/Django Developer

RESPONSIBILITIES:

Executed database queries using Python-MySQL connector and MySQL DB; tested applications with PyUnit and PyTest.

Performed calculations using Python libraries and developed GUI components for front-end functionality.

Created frontend and backend modules using Django, HTML5, CSS3, and Bootstrap 3; implemented backend functionality with Python libraries.

Updated a real-time bidding platform with Python, Tornado, Redis, and built a single-page frontend with ember.js.

Managed MySQL migration projects and developed PL/SQL stored procedures for data migration from Oracle; used JDBC, MySQL, SQL Server, DB2, and Oracle Coherence.

Used plotly.js for histograms and jQuery Datatables for data display; utilized JSON for data serialization with JSP pages.

Implemented SSO/SAML functionality with Spring Security and managed sessions using Redis.

Developed Spring Boot microservices with REST and Kafka; created and consumed REST and SOAP APIs.

Used NetBeans, Eclipse IDE, CVS, and Subversion; automated tasks with AWS CLI, Bash/Python scripts, and ANT for JBOSS deployment.

Integrated with Jenkins, Bitbucket webhooks, and Hipchat; developed Unix Shell scripts for monitoring and report generation.

Conducted regression and smoke testing following Agile-Scrum, Kanban, and Waterfall methodologies.

Technologies Used: Python, Django, ORM, pandas, Tornado, JavaScript, HTML5, CSS3, bootstrap, jQuery, JSON web token, SSO/SAML, Pyramid, Java, MySQL, Rest, Soap, Netbeans, Websphere, CVS, SVN, Junit, Waterfall, AWS EC2, S3, Ant, XML, Jira, Unix, hipchat

Comcast, Palo Alto, CA Sep 2013 – Aug 2015

Python Developer

Responsibilities:

Developed frontend and backend modules using Python with Django, implementing business logic and REST API frameworks, and managed Django projects for maintenance and troubleshooting.

Created Object-Oriented JavaScript modules, debugged with tools like Firebug, and improved AngularJS applications to follow strict MVC patterns for better maintenance.

Built dynamic web pages and implemented business logic with Django; used Python for XML processing and data exchange.

Implemented AJAX functionality with jQuery, JSON, and XML for enhanced data exchange between frontend and backend.

Implemented Python REST API frameworks using Django for effective data retrieval and management.

Automated build scripts using Ant and Maven for Java/J2EE applications and make for C/C++ projects; developed ANT scripts for deployment on JBOSS.

Developed front-end components using AngularJS and Bootstrap; worked extensively with HTML5, CSS3, JavaScript, and jQuery for dynamic web interfaces.

Integrated with back-end RESTful services using AJAX for GET and POST operations; worked on Web Services with REST, WSDL, JSON, and JMS.

Used JUnit for unit, integration, and production testing; managed continuous integration with Jenkins and Bitbucket webhooks.

Developed Unix Shell scripts for system performance monitoring and updates; automated repetitive tasks with AWS CLI and Bash/Python scripts.

Technologies Used:Python, Django, HTML, CSS, Javascript, JQuery, AJAX, Angular.js, Node.js, BootStrap, JAVA, Oracle, MySQL, SQL Server, TOAD, CRUD, PL/SQL, DB2, JDBC, Jira, Git, Tomcat, Rest, SOAP, WebServices, Agile, Water Fall Model, UML, Eclipse, Linux, Unix

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