S&P Global is looking to solve the problem of delivering AI capabilities and advancements to their S&P Global Ratings products and services, and is seeking a GenAI Solutions Architect to develop and implement AI architecture strategies and standards to enhance AI ML model deployment and monitoring efficiency.
Requirements
- 4+ years of hands-on experience in ML architecture design and implementation for large-scale enterprise AI solutions and AI products.
- Experience with business and product stakeholder engagement, collaborating on AI roadmap planning and implementation efforts.
- Experience working in Agile frameworks and delivery methods.
- Expertise in designing and developing complex data-driven architectures for distributed computing and orchestration technology.
- Experience with cloud platform and system architectures.
- Proficiency with technologies for model development and ML operations.
- Knowledge of DevOps, MLOps principles and practices, and experience with version control systems and CI/CD pipelines.
Responsibilities
- AI Architecture Strategy: Develop and implement AI architecture strategies, best practices, and standards to enhance AI ML model deployment and monitoring efficiency.
- ML Architecture Design and Development: Design and develop custom AI architecture for batch and stream processing-based AI ML pipelines, including data ingestion, preprocessing, and scaled AI model computation, ensuring all service level agreements (SLAs) are met.
- Internal Collaboration: Work 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 with business and project management stakeholders in roadmap planning and implementation efforts, ensuring technical milestones align with business requirements.
- AI Infrastructure Architecture: Oversee the design of scalable and reliable infrastructure for AI, ML, and model training and deployment.
- AI Model Deployment Architecture: Lead the architecture of AI model deployment patterns in production environments, ensuring reliability and scalability.
- AI Monitoring Architecture: Design robust monitoring systems to track model performance, data quality, and infrastructure.
Other
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- Experienced professional (8+ years) as an ML engineer, architect, or lead data scientist in a distributed or cloud platform environment, with a desire to assume greater responsibilities as a leader and mentor, while remaining hands-on.
- Occasional travel may be required.
- Right to work in the United States.
- Strong communication and presentation skills to convey technical concepts to non-technical stakeholders.
