Software Engineer AI & Python

MSCI strengthens global markets by connecting participants across the financial ecosystem with a common language.

Our research-based data, analytics and indexes, supported by advanced technology, set standards for global investors and help our clients understand risks and opportunities so they can make better decisions and unlock innovation. We serve asset managers and owners, private-market sponsors and investors, hedge funds, wealth managers, banks, insurers and corporates.

As a global leader in index innovation, we calculate nearly 300,000 equity and fixed income indexes daily5 to help clients reach any goal, integrating factors, thematic insights and sustainability considerations in evolving markets.

1. Lead the design and implementation of machine learning, deep learning, and GenAI systems.
2. Collaborate across engineering, product, domain experts, and senior leadership to identify and solve high-impact problems with AI.
3. Architect clean, maintainable, and efficient ML and data pipelines using modern frameworks and cloud services.
4. Develop and deploy AI models into production environments, ensuring resilience, scalability, and low latency.
5. Stay ahead of the curve in GenAI advancements and infuse them into real-world use cases.
6. Champion best practices in model training, evaluation, interpretability, and governance.
7. Guide and mentor junior team members, fostering innovation and excellence.

1. Strong programming skills in Python, with hands-on experience in ML libraries like TensorFlow, PyTorch, HuggingFace Transformers, Scikit-learn, etc.
2. Proven expertise in building and deploying AI/ML systems at scale (batch or real-time).
3. Practical experience with GenAI techniques including LLMs, fine-tuning, RAG, embeddings, prompt engineering, etc.
4. Solid understanding of MLOps best practices and familiarity with tools like MLflow, Airflow, Kubeflow, and Docker/Kubernetes.
5. Deep knowledge of data structures, algorithms, and software engineering principles.
6. Strong grasp of data modeling, feature engineering, and large-scale data processing with Spark or similar technologies.
7. Experience with cloud platforms (AWS, Azure, GCP) and their respective AI toolkits.
8. Excellent communication skills and ability to translate technical concepts to non-technical stakeholders.
9. Comfortable working in fast-paced environments and navigating ambiguity.
10. Self-motivated and driven by impact, with a strong sense of ownership and initiative.
11. Experience with multimodal AI (text, vision, audio).
12. Publications, open-source contributions, or patents in AI or ML.