Sathvik Veerapaneni

I am an experienced AI professional with expertise in fine-tuning LLMs, deploying machine learning models, and architecting AI pipelines. I have a strong background in implementing state-of-the-art NLP techniques for task requirements and leveraging deep learning frameworks for complex neural network models. Throughout my career, I have demonstrated the ability to ensure efficient deployment and management of ML models in production environments through MLOps. I am skilled in transfer learning, Retrieval-Augmented Generation (RAG), and zero-shot learning, which have enhanced the capabilities and performance of the models I have worked on.

My technical skills include proficiency in Python, C++, Java, Scala, AWS, Azure, GCP, Snowflake, H2O, Auto sklearn, Auto keras, BERT, Mistral, LLaMa, GPT, TensorFlow, PyTorch, NLTK, Word2Vec, Gensim, MySQL, NoSQL, Oracle, CUDA, OpenCL, Flask, Postman, RESTful API, CDC, Data Quality, Data Integrity, Teradata, HVR, GitHub, Jira, and Trello.

I have 4+ years of work experience in machine learning, AI, and NLP. I have built robust machine learning and deep learning models using TensorFlow, Keras, and Apache Spark. I have experience with deep learning frameworks like Caffe and have worked on data preparation, modeling, feature engineering, and production/deployment. I am proficient in predictive and statistical tools, machine learning algorithms, and big data analysis. Additionally, I have experience with neural networks, Naive Bayes, SVM, and decision forests for NLP models such as entity recognition, parts of speech, document similarity, topic modeling, sentiment analysis, recommender systems, and dialog systems. I have also developed statistical machine learning and data mining solutions using R and Python.

I have experience working in both Waterfall and Agile environments, including the Scrum process, and using project management tools like Jira, GitHub, and Git. I have also worked on data replication using the HVR tool and have knowledge of recurrent neural networks and LSTM.