MLOps Engineer II

The MLOps Engineer II will independently design, implement, and manage the automation and streamlining of the machine learning lifecycle, ensuring reliable, efficient, and scalable deployment and monitoring of models. This role requires a solid understanding of DevOps practices, cloud technologies, and machine learning frameworks, with the ability to bridge the gap between data science and operations effectively.

Focus: Independent automation of ML pipelines, deployment strategies, and monitoring in production.

The difference you will make:

• Independently design and implement CI/CD pipelines for machine learning models.

• Automate the process of model training, validation, testing, and deployment using relevant tools. • Develop and maintain automated testing frameworks for machine learning models.

• Develop and manage various model deployment strategies (e.g., A/B testing, canary deployments).

• Build and maintain scalable and reliable infrastructure for model serving (e.g., using Kubernetes, serverless functions).

• Implement and manage model versioning and rollback mechanisms.

• Optimize model serving for latency, throughput, and resource utilization.

• Implement and manage comprehensive monitoring and logging systems to track model performance and identify issues (e.g., model drift, data drift).

• Set up and manage alerting systems to notify the team of performance degradation.

• Contribute to the development and implementation of model governance policies and procedures.

• Ensure compliance with security and privacy requirements.

• Collaborate effectively with data scientists to understand model requirements and dependencies. • Work with software engineers to integrate machine learning models into applications and services.

• Develop and maintain APIs and interfaces for model access.

What you will bring to the role:

Education:

• Bachelor's degree in Computer Science, Engineering, or a related field.

Experience:

• 3+ years of experience in DevOps, software engineering, or a related role with a focus on machine learning deployment and operations. Proven experience with cloud platforms and containerization technologies. Experience in the financial services industry is a plus.

Technical Skills:

• Strong proficiency in DevOps practices and tools (e.g., Jenkins, GitLab CI, Docker, Kubernetes).
• Solid knowledge of cloud computing platforms (e.g., AWS, Azure, GCP) and their machine learning services.
• Knowledge of MLflow or Kubeflow for model management, or similar.
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong experience with scripting and automation (e.g., Python, Bash).
• Solid understanding of software engineering principles and best practices.
• Experience with infrastructure as code (IaC) tools (e.g., Terraform, CloudFormation).

Soft Skills:

• Strong ownership and accountability for assigned work.
• Excellent communication, interpersonal, and conflict resolution skills.
• Effective time management and task prioritization.
• Analytical thinking and problem-solving skills.
• Ability to work independently and collaborate with teams within and across functions.
• Ability to actively listen to customers and accurately understand their needs, expectations, and challenges.
• Adaptability and learning agility in a fast-paced environment.
• Strong attention to detail and a commitment to quality.
• Receptiveness to feedback and continuous improvement mindset.