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Click here or scroll down to respond to this candidateCandidate's Name
(813) 560-3072 EMAIL AVAILABLE https://LINKEDIN LINK AVAILABLE https://github.com/utkarshb95 PROFESSIONAL SUMMARYMachine Learning Engineer with a masters degree in computer science and 3 years of hands-on experience in developing and deploying ML solutions. Expertise in designing and optimizing ETL and ML pipelines, fine-tuning large language models, and implementing production-ready models. Skilled in leveraging advanced statistical analysis and cloud infrastructure to drive innovation and deliver impactful results. Strong problem-solving abilities, collaborative team player, and dedicated to continuous learning and improvement. TECHNICAL SKILLSProgramming Languages: Python, R, C++, JavaScript, Java, SQL, Scala, Linux, REST API, Django, Flask ML & AI: TensorFlow, PyTorch, Scikit-Learn, Keras, XGBoost, NumPy, Pandas, Prophet, ARIMA, NLP, NLTK, OpenCV, Ensemble Methods, CNN, LSTM, Vision Transformer, OpenAI, Generative AI, LLM, RAG, RLHF Data engineering: Azure Data Factory, Synapse, Apache Spark, Hadoop, Kafka, Airflow, ETL, Redshift, Snowflake, BigQuery, PostgreSQL, MongoDB, Cassandra, HDFS, Azure Data Lake, MySQL Data Analysis & Visualization: Excel, Tableau, Power BI, Plotly, Seaborn, Matplotlib Cloud Tools: AWS, Google Cloud, Azure, Azure ML Studio, Databricks, Heroku, Azure DevOps Development Tools: Git, Docker, Kubernetes, Jenkins, VS Code, Jupyter, CI/CD, JIRA, MLFlow, IoT Edge PROFESSIONAL EXPERIENCEMachine Learning Engineer, Gentherm (August 2022 - January 2024) Collaborated with stakeholders to develop ML solutions for automotive thermal technology, achieving 75% energy reduction and improving user comfort with regression algorithms and reinforcement learning. Engineered ETL & ML pipelines for IoT devices, reducing processing time by 50% and enhancing data management with PostgreSQL, Azure Data Lake, and Docker. Implemented CI/CD practices and optimized pipelines using Spark, Azure Data Factory, and Azure ML Studio, improving development efficiency. Deployed and monitored models in production for real-time insights and optimization, applying advanced statistical and time series analysis to reduce user comfort attainment time by 25%. Designed secure cloud infrastructure with Terraform and automated testing and deployment with GitHub CI/CD. Fine-tuned LLM for domain-specific applications, enhancing content generation and information retrieval with RAG, LangChain, nltk, BeautifulSoup, and Flask. Developed an interactive voice-controlled system with GPT-4 LLM, decreasing manual climate adjustments by 30% and improving response time by 25%.Machine Learning Analyst, X2 Analytics (January 2022 - May 2022) Formulated NLP and ML techniques to re-engineer 50% of SQL database entries for anomaly detection. Led data collection and web scraping efforts, providing NLP-driven analytics for B2B clients. Enhanced sentiment analysis by 20% using advanced NLP techniques like NLTK and transformer models. Implemented NLP-based anomaly detection methods, including isolation forests and one-class SVM, reducing false positives by 30% and enhancing data quality assurance. Machine Learning Research Assistant, USF (August 2021 - May 2022) Tailored deep learning models including VAEs, GANs, and vision transformers for multi-object grasping with robotic hand and tactile sensors, achieving 80% accuracy through simulation-to-real fine tuning. Improved neural network accuracy by 20% through feature engineering and fine tuning on large datasets (>100k) from simulation systems, optimizing for robotic manipulation tasks. 2 Experimented with diverse deep learning techniques, reducing computation time by 30% by addressing challenges like imbalanced datasets, noise removal, and dimensionality reduction. Leveraged distributed computing on an HPC system for parallelized simulations and model training across GPU nodes, resulting in a 70% reduction in training time. Data Engineer, 3G Solutions (July 2018 - December 2019) Engineered custom OCR software with OpenCV for automating marksheets evaluation, reducing manual workload by 40% and improving assessment efficiency. Utilized NumPy and Scikit-learn libraries to develop machine learning algorithms for data processing, contributing to a 3x increase in company revenue. Reduced downtime by 25% with predictive maintenance ML modeling including time series analysis and ensemble learning. Spearheaded CNN-based quality control initiatives leveraging OpenCV, resulting in a 40% decrease in defects. EDUCATIONM.S., Computer Science University of South Florida May 2022 (GPA: 3.95/4.00) Thesis Topic: Prediction of the number of objects in a robotic grasp B.Tech., Computer Science Jawaharlal Nehru Technological University Hyderabad, India May 2018 PROJECT EXPERIENCEReinforcement Learning Developed Q-learning neural network and a Double DQN agent beating Atari Pong after 56 hours of training using OpenAI gym with PyTorch.Sequence Prediction Constructed LSTM and GRU models to estimate changes in pouring behavior with Keras and TensorFlow, achieving a 0.01280 RMSE on the test dataset.Image classifier Devised a convolutional neural net for food item state classification with a 77% accuracy rate using PyTorch, involving ETL pipeline, image preprocessing, and performance optimization for ingestion and inference. |
