Staff Engineer (Data Scientist - Machine Learning and Genera

Your new responsibilities Design, develop, and implement machine learning models and generative AI solutions in different domains (e.g. SCM, Manufacturing, Product Management and Corporate Functions) Train, fine-tune, and optimize deep learning models for various applications Work with large-scale datasets to preprocess, clean, and transform data for model training. Design and build robust data pipelines for AI applications. Collaborate with data engineers to optimize data infrastructure and storage for efficient ML processing. Deploy and monitor models in production environments using MLOps best practices. Create various documentations required as part of implementation. Communicate the results to management via data visualizations and presentations. Ability to meet the timelines set by the management on the change requests raised. What we look for Master’s Degree in technical fields like Computer Science, Math, Statistics, Data Science, Artificial Intelligence, Engineering, Physics 5+ years of hands-on experience in Data Science, Machine Learning, and AI. Solid understanding of mathematics, statistics, and probability theory for AI/ML applications. Over 5 years of experience in implementing machine learning solutions using Python, R, or similar languages, with extensive knowledge of various ML algorithms Experience with LLMs Proficiency in Python and ML libraries (e.g., Scikit-learn, Hugging Face, OpenAI API). Hyperparameter tuning, training/validation/testing, evaluation metrics and continuous improvement pipelines Experience with MLOps tools (e.g., MLflow, Kubeflow, Docker, Airflow) and cloud platforms (AWS, GCP, Azure) Hands-on experience working with cloud services such as MS Azure is required Knowledge of data engineering, including working with SQL, NoSQL, and big data technologies Ability to clean and transform messy data in Python Experience with Snowflake SQL for writing efficient queries Experience with DBT for data source understanding Experience with data visualization tools such as Streamlit, Power BI, Qlik Sense, etc.