About the Role:

Grade Level (for internal use):

The Role: GenAI Solutions Architect - Assoc Dir / Dir 

The Team: The Data Science COE at S&P Global in delivers AI capabilities and advancements to our S&P Global Ratings products and services. The AI ML team within the COE is comprised of experts in AI ML modeling, ML engineers and data science and data engineering teams.

Responsibilities include:

• GenAI Architecture Strategy: Develop and implement AI achitecture strategies, best practices, and standards to enhance AI ML model deployment and monitoring efficiency. Develop architecture roadmap and strategy for GenAI Platforms and tech stacks

• ML Architecture Design and Development: Responsible for the design and development of custom AI 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

• 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.

• AI Infrastructure Architecture : Oversee the design of scalable and reliable infrastructure for AI, ML , GenAI, LLM model training and deployment.

• AI Model Deployment Architecture : Lead the architecture of GenAI, LLM , machine learning models deployment patterns in  production environments, with design patterns that ensurie reliability and scalability.

• AI Monitoring Architecture: Create design of GenAI robust monitoring systems to track model performance, data quality, and infrastructure.

• Security and Compliance: Implement security measures and compliance standards to protect sensitive data and ensure adherence to industry regulations.

• Documentation: Maintain comprehensive documentation of AI processes and procedures for reference and knowledge sharing.

• Standards and Best Practices: Ensure the use of standards, governance and best practices in AI 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.

• Serve as a thought leader in generative AI, influencing both technical strategy and executive-level decisions.

• Build and scale production-ready AI systems that operate reliably at enterprise levels, ensuring long-term business impact.

• Exceptional presentation skills to convey technical concepts to non-technical stakeholders. 

• Ability to adapt communication styles to various audiences, from engineers to business stakeholders and executive leadership.

• Passion for staying ahead of AI trends and leveraging emerging technologies.

• Strategic thinker and influencer with demonstrated technical and business acumen and problem-solving skills.

*Occasional travel may be required.

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.

What We’re Looking For:

Basic Required Qualifications:

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

• Experienced professional (8+ years experience) as ML engineer, architect, lead data scientist in Big Data ecosystem or any similar distributed or public cloud platform, with a desire to assume greater responsibilities as a leader and mentor, while still being hands-on

• 4+ years hands-on experience in ML architecture design and implementation for large-scale enterprise AI solutions and AI products

• 4+ year’s experience with Business, Product Stakeholder Engagement and Collaboration: Demonstrat