Requirements

• Expert-level proficiency in Python and/or Java (Spring Boot); solid experience with JavaScript/TypeScript (Angular, Node.js).
• Deep knowledge of AI fundamentals including knowledge representation, automated planning, decision-making under uncertainty, and 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 such as Devin, Claude Code, GitHub Copilot, and Antigravity.
• Strong experience with ML frameworks like TensorFlow and PyTorch, and data science libraries.
• Proven ability to design and expose robust, scalable APIs for AI services.
• 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.
• Hands-on experience with Docker and Kubernetes.
• Exceptional communication skills to articulate complex technical concepts, influence senior stakeholders, and lead technical discussions.
• Strong understanding of banking and regulated financial environments, connecting AI capabilities to measurable business outcomes.
• Progressive software engineering experience with sustained hands-on contributions, especially in AI, LLM-based systems, and agentic architectures.
• Bachelor’s or Master’s degree in Computer Science, AI, Robotics, or a related quantitative discipline.

Nice to Haves

• Advanced data science expertise (statistical modeling, experimentation).
• Experience mentoring and scaling AI-first engineering talent in rapidly evolving toolchains.

What You'll Be Doing

• Lead the design, hands-on implementation, and deployment of scalable agentic AI and generative AI solutions for impactful business use cases.
• Architect advanced agent-based systems involving perception, reasoning, planning, and execution, integrating multiple LLM providers.
• Build and deliver production-grade full-stack AI applications using modern ML/LLM platforms and AI coding tools.
• Champion MVP-driven delivery, rapid iteration, hypothesis testing, and experimentation to achieve fast time-to-value.
• Define rigorous evaluation frameworks and metrics for AI and agent performance to ensure continuous optimization aligned with business goals.
• Evaluate and integrate emerging AI technologies, acting as a thought leader and coach.
• Provide front-line technical leadership, guide architectural decisions, and mentor engineers to foster an AI-first engineering culture.
• Ensure AI solutions are aligned with deep banking domain knowledge and clearly articulate their business and risk impact.