MLOps Engineer

About the role

The MLOps Engineer at Arbitration Forums is responsible for bridging the gap between machine learning model development and their operational deployment. This role ensures models are efficiently running in the production environment and continuously monitored for performance. The MLOps Engineer contributes to the company's AI-powered portfolio by enhancing the scalability and reliability of machine learning applications.

Responsibilities

• Design, implement, and maintain machine learning pipelines and workflows for continuous deployment and integration of machine learning models.
• Optimize pipelines for scalability, efficiency, and cost-effectiveness.
• Collaborate with data scientists and AI engineers to understand model requirements and optimize deployment processes.
• Automate the training, testing, and deployment processes for machine learning models.
• Establish and enforce best practices for version control, documentation, and code quality in ML projects.
• Monitor model performance and optimize algorithms for efficiency.
• Conduct regular maintenance and updates to deployed models.
• Collaborate with cross-functional teams to integrate machine learning solutions into business processes and applications.
• Work with go-to-market, product management, and IT functions as well as stakeholders in AF and its members to identify optimal methods for model rollout and adoption.
• Manage and allocate resources effectively, including computer power and storage for model inference.
• Develop practices and utilize tools for data validation, model testing, and versioning.
• Troubleshoot and resolve machine learning operational issues.
• Document processes, workflows, and best practices for ML Operations.
• Provide technical leadership and mentorship to junior data team members.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Science, or a related field.
• Minimum of 6 years of experience in data science, machine learning, data management, data governance, or a related role.
• Minimum of 6 years as a MLOps Engineer or in a similar role.
• Working knowledge of cloud services (i.e., MS Azure, AWS, Google Cloud).
• Experience with AI tools, such as MS Azure ML, Snowflake, Databricks, CortexAI, Dataiku.
• Deep understanding of data science principles, algorithms, and tools.
• Strong knowledge of data governance, data security, and compliance practices.
• Proficiency in programming languages such as Python, R, or Java.
• Experience with containerization tools like Docker and orchestration tools like Kubernetes.
• Proficiency in ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Working knowledge of CI/CD pipelines, DevOps practices, and automation frameworks.
• Deep understanding of data engineering concepts and tools.
• Familiarity with data visualization and reporting tools (e.g., Webfocus, Power BI).