EY Assessor Tributário Sênior 

Brazil, Minas Gerais 

130785187

Job Title: Senior ML Ops Specialist - Manager

8-12 years

Key Responsibilities:

• Lead the design, implementation, and management of ML Ops pipelines to streamline the deployment, monitoring, and maintenance of machine learning models in production.
• Collaborate with data scientists, engineers, and business stakeholders to ensure seamless integration of ML models into existing systems and workflows.
• Utilize Azure services for deploying, managing, and scaling machine learning applications and infrastructure effectively.
• Monitor model performance, implement strategies for continuous improvement, and optimize model efficiency and reliability.
• Develop and maintain comprehensive documentation for ML Ops processes, workflows, and best practices.
• Implement AI Ops practices to enhance operational efficiency and reliability of AI systems across banking and insurance applications.
• Troubleshoot and resolve complex issues related to model deployment, performance, and data quality.
• Stay updated on industry trends and advancements in ML Ops and AI Ops, providing strategic recommendations for implementation.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• 8-12 years of experience in ML Ops and AI Ops, preferably in the banking and insurance sectors.
• Proficiency in programming languages such as Python or R, with extensive experience in ML frameworks (e.g., TensorFlow, PyTorch).
• Strong understanding of Azure services related to machine learning (e.g., Azure Machine Learning, Azure DevOps).
• Proven experience with CI/CD practices for machine learning model deployment and automation.
• Excellent analytical and problem-solving skills, with a focus on delivering reliable and scalable AI solutions.
• Strong leadership and communication skills, with the ability to work effectively in a collaborative environment.

Preferred Skills:

• Familiarity with containerization technologies (e.g., Docker, Kubernetes) for deploying ML models.
• Knowledge of data governance and compliance standards in the banking and insurance sectors.
• Experience with monitoring and logging tools for AI systems.

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