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AIML Expert
Stargate
  • 3 - 5 yrs
  • 30,000 - 40,000 / month
  • Mumbai
  • CNNs Transformers LSTMs GRUs ARIMA Prophet
    • Full Time
    graduate
    3 - 5 yrs
    30000 - 40000 / month
    5
    Stargate
    Full Time

    Working Type : Work From Office
    Job Description :

    Job Description

    • We are seeking a talented Applied AI/ML Engineer to develop sophisticated models that leverage both visual and time-series data.
    • In this role, you will focus on designing and implementing advanced algorithms to extract meaningful patterns, make accurate predictions, and solve complex analytical challenges.
    • Your primary responsibility will be hands-on model development (for deploying in to automotive domain), from data analysis and feature engineering to training and evaluation.
    • Key Responsibilities: Advanced Model Development Design, build, and validate machine learning models for a variety of complex tasks, including: Computer Vision: Object Detection, Image Segmentation, Facial Analysis, and Classification.
    • Time Series Analysis: Forecasting, Anomaly Detection, and Classification of sequential data.
    • Multi-Modal Modeling: Fusing visual and temporal data to create more powerful predictive models. 
    • Algorithm Implementation and train a wide range of models, including Deep Learning architectures (CNNs, Transformers, LSTMs, GRUs) and traditional statistical methods (e.g., ARIMA, Prophet).
    • Stay current with state-of-the-art techniques and apply them to our unique datasets.
    • Data Analysis & Feature Engineering Conduct in-depth exploratory data analysis to understand data characteristics and identify predictive signals.
    • Develop and implement novel feature engineering techniques tailored to both image and time-series data.
    • Model Optimization & EvaluationFocus on building computationally efficient models.
    • Rigorously evaluate model performance using appropriate metrics and validation strategies to ensure robustness and accuracy.
    • Optimize model architecture and hyperparameters to achieve desired performance targets.
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