AI Engineer - Equities Portfolio Management - TIAA

Key Responsibilities

• Identify and prototype AI opportunities—including agentic and generative models—across the equities platform, collaborating with both technical and business stakeholders.
• Own end‑to‑end delivery of multiple AI initiatives, from concept and data collection through model development, testing, deployment, and monitoring.
• Design, train, and fine‑tune machine‑learning and deep‑learning models (e.g., reinforcement learning, time‑series forecasting) that enhance strategy generation and portfolio rebalancing.
• Integrate AI models into production pipelines using cloud services (AWS) and ensure scalability, reliability, and low‑latency inference for trade execution and advisor‑facing tools.
• Implement robust data‑engineering workflows to ingest, clean, and transform large financial datasets for model training and evaluation.
• Mentor junior engineers, share best practices, and contribute to the AI team’s technical roadmap.

Requirements

• 5+ years of professional experience building and deploying machine‑learning or deep‑learning solutions in a production environment.
• Strong proficiency in Python and frameworks such as PyTorch or TensorFlow.
• Hands‑on experience with cloud platforms (AWS) and containerization/orchestration tools (Docker, Kubernetes).
• Solid understanding of financial data, time‑series analysis, and portfolio management concepts.
• Excellent problem‑solving skills and ability to communicate complex AI concepts to non‑technical audiences.