About This Role
Leading the design, architecture, and delivery of enterprise-scale AI solutions for Investment, Corporate, and Commercial Banking is the core of this AI-first Lead AI Engineer role. It 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 engineer will shape both technical delivery and AI-first engineering culture, partnering closely with multiple technology and business teams.
Responsibilities
- Lead the design, hands-on implementation, and deployment of scalable agentic AI and generative AI solutions for high-impact business use cases
- Architect advanced agent-based systems (perception, reasoning, planning, execution), integrating multiple LLM providers (OpenAI, Anthropic, Google)
- Build and deliver production-grade, full-stack AI applications using modern ML/LLM platforms and AI coding tools (e.g., Devin, GitHub Copilot)
- Champion MVP-driven delivery, rapid iteration, hypothesis testing, and experimentation (A/B testing) to achieve fast time-to-value
- Define rigorous evaluation frameworks and metrics for AI and agent performance, ensuring continuous optimization and alignment with business goals
- Proactively evaluate and integrate emerging AI technologies, particularly in agent-based and autonomous AI systems, while acting as a thought leader and coach
- Provide front-line technical leadership, guide architectural decisions, and mentor engineers across AI, data, and application teams to build an AI-first engineering culture
- Ensure all AI solutions are grounded in deep banking domain understanding and clearly articulate business and risk impact beyond pure technical implementation
Requirements
- Expert-level proficiency in Python and/or Java (Spring Boot)
- Solid experience with JavaScript/TypeScript (Angular, Node.js)
- 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
- Deep expertise in software engineering best practices, including system design, CI/CD, testing strategies, resiliency, security, and operational stability for AI-driven platforms
- Strong experience designing API-first, microservices-based, and event-driven systems, including AI-focused data pipelines and lifecycle management
