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Strong Tier-1 brand and metro location create above-average competition for senior, visible ML leadership roles.
Highly specialized ML/AI and regulated banking domain increase need for domain-specific expertise.
Explicit senior experience thresholds, PhD/MS alternatives, and technical plus compliance requirements make filtering strict.
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Lead research and deployment of LLM-based AI agents handling multi-step workflows, tool use, and multi-agent orchestration for consumer financial tasks.
Own end-to-end lifecycle of AI systems including defining research directions, production scaling, privacy/safety compliance, and collaboration with cross-functional teams to bring solutions to market.
Define and track performance metrics such as task completion rate, accuracy, latency, and customer satisfaction to ensure product impact and safety in a regulated environment.
Ph.D. with 8+ years or M.S. with 12+ years experience building and deploying AI systems in production.
Applied GenAI experience with LLMs including fine-tuning, prompt engineering, and retrieval-augmented generation (RAG).
Experience scaling LLM systems (caching, batching, governance, evaluation).
Proficiency in Python and ML frameworks (PyTorch/TensorFlow, Hugging Face, scikit-learn).
Experienced leader who can set and execute a technical research agenda from concept to production deployment in a high-stakes, regulated financial environment.
Strong background with large language models applied to conversational AI, multi-agent systems, and safety/governance in production.
Data-driven approach with expertise in rigorous experimental design and cross-functional collaboration involving Product, Engineering, Risk, and Design teams.