Lead Applied AI Engineer - Banking Technology, Senior Vice President

We are seeking an AI‑first Lead AI Engineer to define, architect, and deliver enterprise‑scale AI solutions for Investment, Corporate, and Commercial Banking.

This role combines deep hands‑on technical leadership with architectural ownership, driving the adoption of LLMs, agentic workflows, and generative AI platforms to improve efficiency, automation, simplification, and risk reduction. The Lead AI Engineer will shape both technical delivery and AI-first engineering culture, partnering closely with multiple technology and business teams.

• End‑to‑End AI Leadership: Lead the design, hands‑on implementation, and deployment of scalable agentic AI and generative AI solutions for high‑impact business use cases.
• Agentic Architecture: Architect advanced agent‑based systems (perception, reasoning, planning, execution), integrating multiple LLM providers (OpenAI, Anthropic, Google).
• Full‑Stack AI Engineering: Build and deliver production‑grade, full‑stack AI applications using modern ML/LLM platforms and AI coding tools (e.g., Devin, GitHub Copilot).
• Rapid Value Delivery: Champion MVP‑driven delivery, rapid iteration, hypothesis testing, and experimentation (A/B testing) to achieve fast time‑to‑value.
• Evaluation & Optimization: Define rigorous evaluation frameworks and metrics for AI and agent performance, ensuring continuous optimization and alignment with business goals.
• Technology Foresight: Proactively evaluate and integrate emerging AI technologies, particularly in agent‑based and autonomous AI systems, while acting as a thought leader and coach.
• Technical Leadership & Mentorship: Provide front‑line technical leadership, guide architectural decisions, and mentor engineers across AI, data, and application teams to build an AI‑first engineering culture.
• Strategic Domain Alignment: Ensure all AI solutions are grounded in deep banking domain understanding and clearly articulate business and risk impact beyond pure technical implementation.

• Deep knowledge of AI fundamentals (knowledge representation, automated planning, decision‑making under uncertainty, multi‑agent systems).
• Extensive hands‑on experience with LLM platforms (OpenAI, Anthropic, Google), RAG pipelines, MCPs, and agent frameworks (Google ADK).
• Demonstrated effectiveness with AI coding tools (Devin, Claude Code, GitHub Copilot, Antigravity).
• Strong experience with ML frameworks (TensorFlow, PyTorch) and data science libraries.
• Proven ability to design and expose robust, scalable APIs for AI services.