A Machine Learning Engineer trains machine learning (ML) and deep learning (DL) models or implements pretrained models to perform visual recognition tasks, text generation, classification, etc.
- Design & implement ML/DL solutions and integrate them with various Big Data platforms and architectures.
- Creating and maintaining ML pipelines that are scalable, robust, and ready for production.
- Collaborate with domain experts, software developers, and product owners.
- Troubleshoot ML/DL model issues, including recommendations for retrain, revalidate, and improvements/optimization.
- Realize Continuous Integration (CI) and Continuous Deployment (CD) pipelines within ML/DL platforms.
3 years of hands‑on experience in building ML models deployed into real‑world business applications or research.
Good understanding of ML/DL frameworks such as Jupyter Notebook, Anaconda, Tensorflow, Keras, Scikit-Learn, PyTorch, MXNet, etc.
Experience working with cloud services platforms (AWS or Azure) to build ML/DL pipelines; training (GPU CUDA), evaluating, deploying (SageMaker, Docker container).
Proficiency with Python and basic libraries for ML such as scikit-learn and pandas.
Strong working knowledge of ML/DL algorithms (classification, regression, clustering, hyperparameter tuning, etc).
Experience in working with LLM for text and image generation.
Preferences- Having a working knowledge of AI agents is nice to have.
- Experience with Image Processing/Computer Vision is nice to have.
- Experience with Continuous Integration and Continuous Delivery (CI/CD) is nice to have.
We will also factor in relevant certifications e.g., AWS, Azure, Coursera.
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