Director MLOps Engineering

at  SP Global

COMPENSATION/BENEFITS INFORMATION (US APPLICANTS ONLY):

S&P Global states that the anticipated base salary range for this position is 150,000 – 230,000. Final base salary for this role will be based on the individual’s geographical location as well as experience and qualifications for the role.
This role is eligible to receive additional S&P Global benefits. For more information on the benefits we provide to our employees, please click here .

OUR PEOPLE:

We’re more than 35,000 strong worldwide—so we’re able to understand nuances while having a broad perspective. Our team is driven by curiosity and a shared belief that Essential Intelligence can help build a more prosperous future for us all.
From finding new ways to measure sustainability to analyzing energy transition across the supply chain to building workflow solutions that make it easy to tap into insight and apply it. We are changing the way people see things and empowering them to make an impact on the world we live in. We’re committed to a more equitable future and to helping our customers find new, sustainable ways of doing business. We’re constantly seeking new solutions that have progress in mind. Join us and help create the critical insights that truly make a difference.

ABOUT THE ROLE:

Grade Level (for internal use): 13
The Role: Director MLOps Engineering
The Team: The Data Science COE at S&P Global in delivers AI capabilities and advancements to our Ratings products and services. AI ML team comprised of experts in AI ML modeling, ML engineers and data science and data engineering teams.

RESPONSIBILITIES:

MLOps Strategy: Develop and implement MLOps strategies, best practices, and standards to enhance AI ML model deployment and monitoring efficiency. Develop roadmap and strategy for MLOps and LLMOps Platforms and model lifecycle implementation
ML Architecture Design and Development: Responsible for the design and development of custom architecture for batch and stream processing-based AI ML pipelines including data ingestion to preprocessing to scaled AI model compute and ensure the architecture meets all SLA requirements. Work closely with members of technology and business teams in the design, development, and implementation of Enterprise AI platform
Infrastructure Management: Oversee the design, deployment, and management of scalable and reliable infrastructure for model training and deployment.
Model Deployment: Lead the deployment of machine learning models into production environments, ensuring reliability and scalability.
Monitoring and Optimization: Create and maintain robust monitoring systems to track model performance, data quality, and infrastructure health. Identify and implement optimizations to improve system efficiency.
Automation: Develop and maintain automated pipelines for model training, testing, and deployment, optimizing for speed and reliability. Ensure CI-CD best practices are followed.
Internal Collaboration: Collaborate closely with data scientists, machine learning engineers, and software engineers to ensure smooth integration of machine learning models into production systems.
Stakeholder Engagement and Collaboration: Collaborate closely with business and PM stakeholders in roadmap planning and implementation efforts and ensure technical milestones align with business requirements.
Security and Compliance: Implement security measures and compliance standards to protect sensitive data and ensure adherence to industry regulations.
Mentorship: Recruit, develop and mentor technical AI/ML engineering talent on the team Provide guidance and mentorship to junior MLOps engineers, fostering their professional growth and development.
Documentation: Maintain comprehensive documentation of MLOps processes and procedures for reference and knowledge sharing.
Standards and Best Practices: Ensure the use of standards, governance and best practices in ML pipeline monitoring and ML model monitoring, and adherence to model and data governance standards
Problem Solving: Troubleshoot complex issues related to machine learning model deployments and data pipelines, and develop innovative solutions.

OUR PURPOSE:

Progress is not a self-starter. It requires a catalyst to be set in motion. Information, imagination, people, technology–the right combination can unlock possibility and change the world.
Our world is in transition and getting more complex by the day. We push past expected observations and seek out new levels of understanding so that we can help companies, governments and individuals make an impact on tomorrow. At S&P Global we transform data into Essential Intelligence®, pinpointing risks and opening possibilities. We Accelerate Progress.