VP, Lead AI Engineer - Agentic AI for Networking

About the role

DBS Group Technology is looking for an experienced and visionary Lead AI Engineer to drive the development and implementation of advanced AI solutions, with a particular focus on Agentic AI to optimise the bank's networking infrastructure. You will lead a team in designing, building and optimising AI-powered applications that deliver business value, and shape the AI strategy and execution within the networking domain.

Key responsibilities

• Lead the design, development and deployment of agentic AI solutions for network automation, predictive maintenance, anomaly detection and intelligent network management.
• Architect end-to-end Retrieval-Augmented Generation (RAG) workflows, agentic AI systems and other AI solutions for networking environments, ensuring scalability, efficiency and robustness.
• Partner with networking stakeholders to define AI initiatives and turn strategic goals into technical requirements for network-focused AI agents.
• Mentor a team of AI engineers and foster a culture of innovation and continuous improvement.
• Set best practices for AI model evaluation, optimisation and monitoring, and ensure compliance with performance standards, risk management guidelines and regulatory requirements.
• Drive research and adoption of emerging AI technologies and recommend strategic advances for the AI roadmap.
• Lead troubleshooting of complex AI issues in network environments and ensure high-quality, well-documented solutions.

Required profile

• Proven track record developing and deploying scalable AI applications, with a strong foundation in Python and experience building agentic AI systems.
• Prior experience leading technical teams, mentoring engineers and delivering complex AI projects.
• Strong analytical, problem-solving and critical thinking skills with meticulous attention to detail.
• Preferred: ability to spot strategic opportunities for agentic AI to automate and optimise network processes.

Required skills

• Advanced proficiency with leading AI frameworks, including LLM orchestration, vector databases, prompt engineering and AI agent frameworks.
• Demonstrated expertise in Retrieval-Augmented Generation (RAG) systems.
• DevOps for AI and MLOps, including CI/CD pipelines for model deployment, monitoring and management.
• Cross-platform development across Unix and Windows environments.