Machine Learning Engineer

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.

Requirements:

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).