Full-Time Lead Generative AI Machine Learning Engineer
S&P Global is hiring a remote Full-Time Lead Generative AI Machine Learning Engineer. The career level for this job opening is Expert and is accepting USA based applicants remotely. Read complete job description before applying.
S&P Global
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About the Role:
Grade Level (for internal use):
11
About the Role:
We are seeking a Lead ML Engineer to join our ML team within the Data Science COE at S&P Global. As a Lead ML Engineer, you will contribute to the deployment, monitoring, and management of machine learning models and data pipelines. You will work with a peer group of ML engineers to develop ML modules and end-to-end engineering solutions.
In this role, you will play a pivotal role in implementing our machine learning engineering operations, ensuring the seamless deployment, monitoring, and management of our machine learning models and data pipelines.
The Team:
You will be work closely in a world class AI ML team comprised of experts in AI ML modeling, ML engineers and data science and data engineering teams. You will contribute to engineering and developing solutions for ML operations and be a critical part of leading S&P's AI-driven transformation to drive value internally and for our customers.
S&P is a leader in automation and AI/ML to transform risk management. This role is a unique opportunity for ML/LLMops engineers to grow into the next step in their career journey.
Responsibilities and Impact:
- Architect, develop and manage machine learning model development and deployment lifecycle to launch GenAI and ML services end to end.
- Work on large-scale stateful and stateless distributed systems, including infrastructure, data ingestion platforms, SQL and no-SQL databases, microservices, orchestration services and more.
- Collaborate with cross-functional teams to integrate machine learning models into production systems.
- Create and manage Documentation and knowledge base, including development best practices, MLOps/LLMOps processes and procedures.
- Workclosely with members of technology teams in the development, and implementation of Enterprise AI platform.
- Fine Tune and Optimize Models: Adjust and refine generative AI models to enhance performance, adapt to new data, or meet specific use case requirements.