AI Engineer

MSCI Data AI Engineering is a central team responsible for building and managing a broad suite of data-centric applications and capabilities that support all MSCI business verticals.

A core part of our mission is ensuring data quality, consistency, and reliability across MSCI's products. The team designs and implements robust AI-driven data quality enhancements to guarantee that downstream analytics, models, and client-facing solutions are built on trusted data.

As a member of this team, you will work alongside highly motivated professionals who challenge one another to produce best-in-class products and technology. We stay at the forefront of advancements in LLMs, GenAI, NLP, ML, and modern data science - and lead the planning and deployment of advanced AI and data quality solutions across MSCI's application landscape.

Your Key Responsibilities:

• Develop data science applications and contribute to MSCI's ongoing AI adoption journey.
• Implement ML/NLP/GenAI/Agentic AI, and advanced anomaly detection methodologies (including statistical, ML-based, and AI-driven approaches) within data science tools to deliver new functionality for both the internal production platform and the client delivery platform.
• Support teams across data and technology in implementing AI solutions and integrating their services with MSCI's data science products and platforms.
• Engage with content expert teams across technology and build a deep understanding of MSCI APIs and data delivery platforms.
• Collaborate closely with Product Management, Quality Assurance, Data Operations, and IT Infrastructure throughout all stages of the development lifecycle, ensuring reliable, high-quality, and scalable solutions.

Your skills and experience that will help you excel:

• At least a Bachelor's or Master's degree in Computer Science, Mathematics, Engineering, or a related field.
• 5 years of experience in finding patterns in data, creating statistical models, data exploration, data visualization, and data mining.
• Experience in developing AI and ML solutions and delivering them to production.
• Strong hands-on experience in Python and NLP.
• Expertise in anomaly detection methodologies, including statistical techniques, ML-based approaches, and AI-driven detection systems for identifying irregularities, data drift, and quality issues.
• Prior experience or projects involving LLMs and Agentic AI is a strong plus.
• Understanding of good software development practices: Test-Driven Development (TDD), refactoring, pair-programming, and solid design principles.
• Self-motivated, proactive, and demonstrating a strong drive toward delivery.
• Experience with cloud-based solutions, especially Azure or Google Cloud Platform (GCP).
• Knowledge of DevOps practices and tools for continuous integration and deployment.