Job Description

Key Responsibilities:

Understanding business objectives and developing models and the system that help to achieve them, along with metrics to track their progress Managing available resources such as hardware, data, and personnel so that deadlines are met Analyzing the Deep Learning, ML algorithms that could be used to solve a given problem and ranking them by their success probability Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world Demonstrable history of devising and overseeing data-centred projects Verifying data quality, and/or ensuring it via data cleaning Supervising the data acquisition process if more data is needed Finding available datasets online that could be used for training Defining validation strategies, feature engineering data augmentation pipelines to be done on a given dataset Training models and tuning their hyperparameters Analysing the errors of the model and designing strategies to overcome them Deploying models to production Ability to relay insights in layman's terms, such that these can be used to inform business decisions. Collaborate with cross-functional teams, including SAP Application team, customers to define requirements and implement AI solutions. Establish and enforce data governance standards implement best practices for data privacy and protection in AI applications

Qualifications and Education Requirements:

Bachelor's/Master's degree in computer science, data science, mathematics or a related field. At least 6-8 years' experience in building Al/ML applications

Preferred Skills:

Proficiency in statistical techniques such as hypothesis testing, regression analysis, clustering, classification, and time series analysis to extract insights and make predictions from data. Proficiency with a deep learning framework such as TensorFlow, PyTorch and Keras Proficiency with OpenCV, Django Worked on LLM models, Proficiency in data visualization tools and techniques to create clear and insightful visualizations (e.g., matplotlib, seaborn, plotly, ggplot2, Tableau) for communicating findings and insights to stakeholders. Ability to engineer and select relevant features from raw data to improve model performance and accuracy, including domain-specific feature creation and dimensionality reduction techniques. Worked on Big data platforms and technologies such as Apache Hadoop, Spark, Kafka, or Hive for processing and analyzing large volumes of data efficiently and at scale. Familiarity in working and deploying applications on Ubuntu/Linux system Has lead the team of Al/ML engineers * Excellent communication, negotiation, and interpersonal skills.