Lead Generative AI Machine Learning Engineer

$108,000 - $210,000/Yr

S&P Global

Full-time - Mid Level

10,001+ employees

Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services

About the position

The Lead Generative AI Machine Learning Engineer at S&P Global will be responsible for the deployment, monitoring, and management of machine learning models and data pipelines. This role involves collaborating with a team of ML engineers to develop ML modules and end-to-end engineering solutions, playing a critical part in the company's AI-driven transformation efforts.

Responsibilities

• 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.
• Work closely with members of technology teams in the development and implementation of Enterprise AI platform.
• Fine tune and optimize models to enhance performance, adapt to new data, or meet specific use case requirements.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 8+ years of progressive experience as a data analytics, machine learning engineer or similar roles.
• A minimum of 5 years of experience in data science, data analytics, or related field.
• 5 years of relevant experience with writing production level, scalable code with Python (or Scala).
• Experience in MLOps/LLMOps, machine learning engineering, Big Data, or a related role.
• Proficiency with Elasticsearch, SQL, NoSQL, Apache Airflow, Apache Spark, Kafka, Databricks, MLflow.
• Experience with containerization, Kubernetes, cloud platforms, CI/CD and workflow orchestration tools.
• Experience in distributed systems programming, AI/ML solutions architecture, Microservices architecture.

Nice-to-haves

• 2-3 years of experience with operationalizing data-driven pipelines for large scale batch and stream processing analytics solutions.
• Experience with contributing to open-source initiatives or in research projects and/or participation in Kaggle competitions.
• 6-12 months of experience working with RAG pipelines, prompt engineering and/or Generative AI use cases.

Benefits

• Health care coverage designed for the mind and body.
• Generous time off helps keep you energized for your time on.
• Access to a wealth of resources to grow your career and learn valuable new skills.
• Secure your financial future through competitive pay, retirement planning, and financial wellness programs.
• Best-in-class benefits for families, including perks for partners and children.
• Retail discounts and referral incentive awards.