Bangalore, Karnataka, India
Finance & Banking
Full-Time
CoreOps.AI
Overview
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
- 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
- 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.
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