Head of Artificial Intelligence

Morgan McKinley; Singapore; On-site; SGD 350,000 - 650,000; Full time

Job summary

Morgan McKinley is seeking a senior AI leader to build and scale an enterprise AI engineering organisation, while staying hands-on in technical strategy, architecture and delivery. You will lead multidisciplinary teams of AI engineers, ML engineers and data scientists to deliver scalable GenAI and Agentic AI solutions.

You will establish AI/MLOps, governance and responsible AI practices, drive productionisation, and partner with executives to translate AI opportunities into measurable business

Qualifications

• Extensive leadership experience building and scaling enterprise AI capabilities, platforms or products.
• Strong technical depth across multiple generations of AI, including traditional ML, deep learning, Generative AI, LLMs and Agentic AI.
• Proven experience designing and deploying production-grade AI/ML and GenAI systems at enterprise scale.
• Strong understanding of AI architecture, MLOps/LLMOps/AgentOps, model evaluation, observability, governance and responsible AI.
• Experience leading multidisciplinary teams of AI/ML engineers, data scientists and applied AI specialists.
• Ability to remain hands-on and provide technical direction on architecture, models, data, engineering practices and AI system performance.
• Strong product and commercial mindset, with a track record of translating AI opportunities into measurable business outcomes.
• Excellent stakeholder management and communication skills, with the ability to influence senior executives and navigate complex enterprise environments.

Responsibilities

• Define and execute the enterprise AI capabilities and services roadmap, aligned with business strategy and measurable outcomes.
• Build reusable AI platforms, services, frameworks, reference architectures and engineering standards across AI/ML, GenAI and Agentic AI.
• Lead and develop multidisciplinary teams spanning AI engineers, ML engineers, data scientists and applied AI specialists.
• Architect and scale enterprise-grade GenAI and Agentic AI solutions, including orchestration, RAG, tool use, memory, evaluation, safety and human oversight.
• Provide hands-on technical leadership across model architecture, data pipelines, training, feature engineering and AI/agent performance.
• Drive the productionisation of AI solutions, ensuring scalability, reliability, security, cost efficiency and maintainability.
• Establish AI/MLOps, LLMOps and AgentOps practices covering deployment, monitoring, evaluation, incident management and continuous improvement.
• Identify and prioritise AI opportunities and investments, including technology selection, build-vs-buy decisions and resource allocation.
• Partner with technology, data, security, governance and business stakeholders to drive enterprise-wide AI adoption and execution.
• Establish responsible AI, governance, risk and deployment standards while balancing innovation with long-term platform health.

Skills

Leadership

AI Architecture

GenAI & Agentic AI

Stakeholder Management

Job description

*Overseas applicants outside of Singapore are welcome to apply.

Company Summary

A leading technology organisation is accelerating its enterprise-wide AI transformation, building scalable AI capabilities that can be adopted across multiple business functions. The organisation is investing across AI/ML, Generative AI, Agentic AI, optimisation, digital twins, IoT and conversational analytics.

This is a senior leadership role with responsibility for building and scaling a high-performing AI engineering organisation, while remaining hands‑on in technical strategy, architecture and delivery.

• Define and execute the enterprise AI capabilities and services roadmap, aligned with business strategy and measurable outcomes.
• Build reusable AI platforms, services, frameworks, reference architectures and engineering standards across AI/ML, GenAI and Agentic AI.
• Lead and develop multidisciplinary teams spanning AI engineers, ML engineers, data scientists and applied AI specialists.
• Architect and scale enterprise‑grade GenAI and Agentic AI solutions, including orchestration, RAG, tool use, memory, evaluation, safety and human oversight.
• Provide hands‑on technical leadership across model architecture, data pipelines, training, feature engineering and AI/agent performance.
• Drive the productionisation of AI solutions, ensuring scalability, reliability, security, cost efficiency and maintainability.
• Establish AI/MLOps, LLMOps and AgentOps practices covering deployment, monitoring, evaluation, incident management and continuous improvement.
• Identify and prioritise AI opportunities and investments, including technology selection, build‑vs‑buy decisions and resource allocation.
• Partner with technology, data, security, governance and business stakeholders to drive enterprise‑wide AI adoption and execution.
• Establish responsible AI, governance, risk and deployment standards while balancing innovation with long‑term platform health.

Requirements

• Extensive leadership experience building and scaling enterprise AI capabilities, platforms or products.
• Strong technical depth across multiple generations of AI, including traditional ML, deep learning, Generative AI, LLMs and Agentic AI.
• Proven experience designing and deploying production‑grade AI/ML and GenAI systems at enterprise scale.
• Strong understanding of AI architecture, MLOps/LLMOps/AgentOps, model evaluation, observability, governance and responsible AI.
• Experience leading multidisciplinary teams of AI/ML engineers, data scientists and applied AI specialists.
• Ability to remain hands‑on and provide technical direction on architecture, models, data, engineering practices and AI system performance.
• Strong product and commercial mindset, with a track record of translating AI opportunities into measurable business outcomes.
• Excellent stakeholder management and communication skills, with the ability to influence senior executives and navigate complex enterprise environments.
• Experience with areas such as optimisation, digital twins, IoT or conversational AI would be advantageous.