Vidhyullatha Thota

About Vidhyullatha Thota

With more than 8 years of experience working as a Python and Machine Learning Engineer, I have built a strong foundation in designing and deploying end to end AI solutions. My work has centered around developing scalable ML pipelines, building advanced deep learning models, and transforming complex datasets into meaningful insights. I enjoy tackling challenging problems and creating solutions that make a measurable difference in business operations and user experience.My technical background includes deep expertise in Python, machine learning, deep learning, NLP, computer vision, and time series forecasting. I am proficient in frameworks such as TensorFlow, PyTorch, and scikit learn, and have extensive experience with distributed computing using Spark and Hadoop. I have deployed machine learning models through APIs using Flask and FastAPI, implemented full MLOps workflows including CI/CD, model monitoring, and versioning, and worked across major cloud platforms including AWS, GCP, and Azure. I also enjoy building data visualizations and dashboards that help teams understand trends and make informed decisions.I am passionate about writing clean and scalable code, mentoring team members, and staying current with the latest advancements in AI and machine learning. I am always looking for opportunities to bring technical excellence, creativity, and problem solving together to build impactful AI powered solutions. I am currently open to new opportunities where I can continue applying my skills and growing in a dynamic, forward thinking environment.

Experience

1. Ai Ml Engineer

   JPMorganChase

   Aug 2024 — Present · OH, US

   I worked on a large-scale AI and machine learning initiative focused on automating data workflows and improving predictive analytics across multiple business functions. The project involved processing high-volume structured and unstructured data and building models that supported real-time insights for decision-making, risk analysis, and anomaly detection.My responsibilities included developing supervised and unsupervised ML models, deep learning architectures such as CNNs, RNNs, LSTMs, and Transformers, and NLP pipelines for sentiment analysis, entity extraction, and document classification. I also contributed to computer vision solutions for document processing and quality checks. Throughout the project, I focused on writing modular Python code, optimizing model performance, and creating clear visualizations to support stakeholders.To ensure production readiness, I implemented full MLOps practices including CI/CD pipelines, MLflow tracking, model versioning, and automated monitoring. I deployed ML models through Flask and FastAPI APIs and worked with distributed data systems like Spark and Hadoop. The project also required extensive cloud integration, and I deployed solutions across AWS, GCP, and Azure to ensure scalability, reliability, and seamless integration with existing systems.

Education

• JNTUH College of Engineering Hyderabad

  Bachelor of Technology - BTech, Computer Science