VP/AVP, Lead AI Engineer - Agentic AI for Networking

About the role

DBS Group Technology is looking for an experienced Lead AI Engineer to drive the development and implementation of advanced AI solutions, with a 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.