AI/ML Expert

On-siteSeniorML EngineerJust posted

✦Role summaryAI-generated

The role involves designing and implementing advanced computer vision and time‑series models for automotive applications, including object detection, segmentation, forecasting, and anomaly detection. You will also create multi‑modal pipelines that fuse visual and temporal data and handle end‑to‑end model development from feature engineering to deployment.

Skills required

Python Deep learning Computer vision Object detection Image segmentation Time series forecasting Anomaly detection Feature engineering Multi-modal learning AI Time series Data analysis Machine learning

About this role

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.