Machine Learning Ops

Summary

Join Foxbox Digital as an MLOps Engineer and play a key role in operationalizing machine learning models for our clients. You will design, develop, and implement scalable data and model deployment pipelines, monitor model performance, and automate ML workflows using CI/CD. Collaboration with data scientists and engineers is crucial to deliver successful AI/ML solutions. This remote-first position requires a Bachelor's or Master's degree in a relevant field and 3+ years of experience in MLOps or related areas. You'll need expertise in Python or R, ML frameworks, cloud technologies, and CI/CD processes. Foxbox offers continuous training, a collaborative culture, and the chance to work on impactful projects.

Requirements

• Hold a Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related field
• Have 3+ years of experience in machine learning operations, model deployment, or related experience
• Have expertise in programming languages such as Python or R
• Be familiar with ML frameworks (e.g. TensorFlow, PyTorch, Scikit-learn)
• Have experience in cloud technologies like AWS, Google Cloud, or Azure
• Possess strong skills in version control (e.g. Git) and CI/CD processes
• Have a solid understanding of containerization technologies such as Docker and orchestration systems like Kubernetes
• Have adept problem-solving skills and the ability to work collaboratively in cross-functional teams
• Have excellent written and verbal communication skills to convey complex technical concepts clearly
• Have a proactive attitude towards learning and adapting to new technologies

Responsibilities

• Design, develop, and implement scalable pipelines for data and model deployment in production environments
• Monitor and maintain the performance of deployed models, ensuring they meet specified accuracy and robustness standards
• Automate ML Workflow processes for continuous integration and continuous delivery (CI/CD) related to ML models
• Collaborate with data scientists to transition models from development to production
• Ensure compliance with data privacy regulations and best practices in machine learning operations
• Develop tools for monitoring and visualizing model performance and data drift
• Conduct regular audits of ML systems to identify improvement opportunities
• Train team members on best practices for deploying and maintaining machine learning models

Preferred Qualifications

• Experience with MLOps or tools such as MLflow, Kubeflow, or Airflow is a plus
• Knowledge of data engineering and ETL processes is an advantage

Benefits

• We offer continuous training and growth opportunities
• Remote-first environment with a culture of collaboration and innovation
• Opportunity to work on a project that directly impacts business success
• You are part of a multicultural and collaborative team that is constantly growing