Machine Learning Engineer

Key Skills

Aartificial intelligence, Apache Flink, AWS, Azure, C Programming, C++, Data science techniques, Databricks, Effective communication skills, Google Cloud Platform (GCP), Java Programming, Kubernetes-K8s, Machine learning techniques, MLflow, MLOps tools, NoSQL, Perl Programming, Python Programming, R Programming, Scala Programming, SPARK Programming, SQL

Job Description

The Team: The Data Science/AI/ML team is a newly formed innovation and deployment team within S&P Global Ratings that will be responsible for building and executing a bold vision around using Machine Learning, Artificial Intelligence, Data Science, knowledge engineering, and human computer interfaces for augmenting various business processes.

The Impact: This role will have a significant impact on the success of our data science, AI, and Machine Learning projects ranging from choosing which projects should be undertaken, to delivering highest quality solution, ultimately enabling our business processes and products with AI and Machine Learning solutions.

What’s in it for you: This is a high visibility role with an opportunity to make a very meaningful impact on the future direction of the company. You will work with senior leaders in the organization to define, build, and transform our business.

Responsibilities:

•     ML Model Deployment: Assist in deploying machine learning models into production environments. 
•     AI ML pipeline and model Monitoring: Contribute to the monitoring and maintenance of model performance and infrastructure health. 
•     Automation: Participate in the development and maintenance of automated MLOps pipelines. 
•     Collaboration: Collaborate with cross-functional teams to integrate machine learning models into production systems. 
•     Documentation: Maintain documentation of MLOps processes and procedures. 
•     Work closely with members of technology teams in the development, and implementation of Enterprise AI platform 
•     Problem Solving: Assist in troubleshooting and resolving MLOps-related issues. 

Qualifications:

• BS (or higher) in Computer Science, engineering, mathematics or related discipline or 5+ years of relevant professional experience in Machine Learning/Artificial Intelligence with a solid foundation in software engineering, excellent design principles, best-in-class code habits, and experience working on advanced models. 
• Experience working with business stakeholders and users, providing research direction and solution design and writing robust maintainable architectures and APIs.
• At least 2 years of experience designing/building data-intensive solutions using distributed computing.
• 1-3 years experience with Python, Java, Kubernetes, and data and workflow orchestration tools
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform.
• Proficient programming skills at a high-level language (e.g. Java, Scala, Python, C/C++, Perl, R
• Solid knowledge of at least one machine learning research frameworks
• Familiarity (ideally 1-2+ years’ experience) with containerization, scripting, cloud platforms, and CI/CD.
• 1-3 years’ experience with Python, Java, Kubernetes, and data and workflow orchestration tools
• 1+ years’ experience with Elasticsearch, SQL, NoSQL,  Apache spark, Flink, Databricks and Mlflow.
• Prior 1-3 experience with operationalizing data-driven pipelines for large scale batch and stream processing analytics solutions
• Good to have experience with contributing to GitHub and open source initiatives or in research projects and/or participation in Kaggle competitions
• Ability to quickly, efficiently, and effectively define and prototype solutions with continual iteration within aggressive product deadlines.
• Demonstrate strong communication and documentation skills for both technical and non-technical audiences.

Preferred Qualifications:

You are a Software Engineer with Data Science experience or a Data Scientist with Software Engineering experience. You have deployed advanced models in production environments. You have created models that drive performance in complex situations and have helped to define and implement best practices and standards. You are comfortable collaborating with and educating other Machine Learning Engineers on the team.