2027 Graduate Talent Program - Artificial Intelligence Engineer

About the role

A 24-month graduate program for aspiring Artificial Intelligence Engineers. You will design, develop, deploy, evaluate, and improve AI-powered solutions, including agents and generative AI applications, while collaborating with engineers, product managers, architects, and business stakeholders across the organization.

Responsibilities

• Design and develop AI agents that automate, augment, and optimize business processes across multiple domains.
• Build generative AI applications using prompt engineering, RAG, embeddings, knowledge retrieval, MCP, LangGraph, and agentic frameworks.
• Translate business challenges into scalable AI solutions by analyzing workflows and identifying automation opportunities.
• Integrate AI agents with enterprise systems, APIs, databases, and business applications.
• Contribute across design, development, evaluation, deployment, monitoring, and continuous improvement throughout the AI solution lifecycle.
• Design evaluation frameworks and apply responsible AI principles supporting quality, reliability, safety, governance, and business impact.

Team and Program

• Join the Chief AI Office, working alongside AI engineers, software developers, architects, product managers, and business experts.
• Contribute to scalable AI platforms, intelligent agents, and reusable capabilities designed to create measurable business impact.
• Gain experience through structured learning, mentorship, hands-on delivery, networking, and opportunities to participate in rotations.

Requirements

• Graduate between September 2025 and September 2027, with a relevant technical discipline and interest in financial services.
• Understand software engineering principles, algorithms, data structures, and system design fundamentals.
• Demonstrate proficiency in Python and familiarity with version control, testing, and CI/CD concepts.
• Understand LLMs, generative AI architectures, embeddings, vector databases, RAG, MCP, and orchestration frameworks such as LangGraph.
• Have built AI or machine learning applications through academic projects, hackathons, research, or other practical work.
• Analyze processes, solve complex problems, evaluate trade-offs, and translate business needs into practical technology solutions.