Machine Learning Operations Engineer II

About the role

This position offers growth, flexibility and a collaborative work environment. We're looking for a motivated and curious Machine Learning Operation Engineer to join our Data Science Team.

Responsibilities

• Aid in the design and development of MLOps infrastructure to support the deployment, management, and monitoring of machine learning models within the firm.
• Collaborate with data scientists and engineers to ensure seamless integration of data science and engineering processes.
• Support maintenance of automated workflows and pipelines for efficient model deployment and monitoring.
• Troubleshoot and resolve any issues with model performance, data pipelines, or infrastructure.
• Assist with ensuring the security and scalability of the MLOps infrastructure to handle large volumes of data and models.
• Support maintenance of documentation for MLOps processes and systems.
• Collaborate with cross-functional teams to understand business needs and translate them into technical requirements.
• Ensure compliance with data security and privacy regulations.
• Stay up to date with industry trends and advancements in machine learning and MLOps.

Requirements

• 2+ years' experience as a Data/ML Engineer
• Bachelor's degree is required. Combination of relevant experience, education, and training may be accepted in lieu of degree.

Qualifications

Experience with cloud platforms (e.g., AWS, Azure, GCP), basic understanding of SQL, experience with ETL pipeline design and maintenance, knowledge of CI/CD concepts and experience with tools like Jenkins, Git, Perforce, etc., proficiency in programming languages such as Python, knowledge of data pipeline architecture and data transformation, knowledge of machine learning model deployment and management, familiarity with performance monitoring and optimization techniques, and basic understanding of machine learning and DevOps principles are required.

Skills

• Familiarity with cloud platforms (e.g., AWS, Azure, GCP)
• Basic understanding of SQL and experience with ETL pipeline design and maintenance
• Knowledge of CI/CD concepts and experience with tools like Jenkins, Git, Perforce, etc.
• Proficiency in programming languages such as Python
• Knowledge of data pipeline architecture and data transformation
• Knowledge of machine learning model deployment and management
• Familiarity with performance monitoring and optimization techniques
• Basic understanding of machine learning and DevOps principles

Benefits

To view a complete list of benefits, click here.

Pay

Competitive compensation package including base salary, bonus, and benefits.

Schedule

Full-time position with flexible working hours.