Machine Learning Engineer

We are seeking a highly motivated Machine Learning Engineer to join our growing team. In this role, you will collaborate with data engineers, scientists, and developers to enhance the quality and capabilities of our Machine Learning solutions, including their lifecycle management (MLOps). You will also be involved in discovering innovative ways to present our data to stakeholders and interacting with them to understand better how our data can generate valuable insights.

 

Responsibilities:

Independently lead complex machine learning projects from inception to deployment

Partner with data scientists to define data requirements for machine learning models and ensure high-quality data for training and evaluation.

Collaborate with the product owner and data engineering team to refine features and user stories that drive the development of our Data Lake and MLOps capabilities.

Select, implement, and compare different machine learning algorithms for specific tasks, considering factors like model complexity, interpretability, and computational efficiency.

Develop, train, and evaluate machine learning models using relevant libraries and frameworks (eg, TensorFlow, PyTorch, scikit-learn)

Conduct exploratory data analysis (EDA) and feature engineering to identify and extract relevant features for model training.

Conduct hyper-parameter tuning and feature engineering to optimize model performance.

Automate model retraining pipelines to ensure models stay up-to-date with changes in the underlying data distribution. Deployment of machine learning algorithms/models.

Deploy machine learning models into production environments and monitor their performance.

Collaborate with data engineers to design and implement scalable data pipelines and infrastructure.

Ensure data quality and consistency throughout the machine learning lifecycle.

Spearhead and mentor junior engineers, fostering their technical growth and development.

Contribute to the development and documentation of technical standards and best practices.

Contribute to an agile environment focused on continuous improvement and innovation

 

Qualifications:

3-5 years of hands-on experience in machine learning engineering, with a proven track record of delivering successful projects.

Proficiency in Python and at least one deep learning framework (eg, TensorFlow, PyTorch).

Strong understanding of machine learning algorithms and techniques (eg, regression, classification, clustering, neural networks, reinforcement learning).

Experience with cloud platforms (eg, AWS, GCP, Azure) and cloud-based machine learning services.

Knowledge of data engineering concepts and tools (eg, ETL pipelines, data warehousing).

Familiarity with version control systems (eg, Git) and CI/CD pipelines.

Excellent communication, collaboration, and problem-solving skills.

Ability to work without supervision and as part of a team.