AI Engineer

AI & Data Science Application Development
• Design, develop, and maintain data science and AI-driven applications that support multiple MSCI business lines.

• Implement machine learning, NLP, and GenAI solutions that move from experimentation to production.

• Build and integrate LLM-powered capabilities such as retrieval-augmented generation (RAG), summarization, classification, extraction, and reasoning into enterprise platforms.

GenAI, LLMs & Agentic AI
• Develop and enhance solutions using LLMs, LangChain, LangGraph, and agentic AI frameworks.

• Apply best practices in prompt engineering, orchestration, evaluation, and monitoring to ensure consistent, reliable AI outputs.

• Work with structured and unstructured data, including text processing, embeddings, and vector-based retrieval.

Platform Integration & Cross-Functional Collaboration
• Support teams across Data, Technology, and Engineering to integrate AI services with MSCI's data science platforms and AP!s.

• Engage with content expert teams to understand MSCI datasets, data delivery platforms, and business workflows.

• Collaborate closely with Product Management, Quality Assurance, Data Operations, and IT Infrastructure across all stages of the development lifecycle.

Production Readiness & Engineering Excellence
• Ensure AI solutions meet enterprise standards for scalability, performance, reliability, and maintainability.

• Follow strong software engineering best practices, including test-driven development, code reviews, refactoring, and clean system design.

• Contribute to CI/CD pipelines and DevOps processes to enable continuous integration, deployment, and monitoring of AI

systems.

AI Governance, Risk & Compliance
• Ensure AI and GenAI solutions comply with MSCI's data governance, security, and responsible AI standards.

• Implement controls to support model transparency, traceability, auditability, and reproducibility, especially for LLM-based systems.

• Apply guardrails to mitigate hallucinations, bias, data leakage, and misuse in AI outputs.

• Ensure appropriate handling of confidential, sensitive, and regulated data in accordance with firm policies.

• Partner with Risk, Legal, Compliance, and Information Security teams to support reviews, audits, and regulatory requirements related to AI systems.