Head of AI Engineering & Platforms

Gravitas Recruitment Group (Global) Ltd; Singapore; On-site; SGD 300,000 - 460,000; Full time

Job summary

Gravitas Recruitment Group (Global) Ltd in Singapore is seeking a senior AI engineering leader to shape and expand enterprise-wide AI capabilities. You will own the AI strategy, set the technical direction, and directly influence architecture, standards and delivery across multiple business units.

You will lead a multidisciplinary team of AI engineers, data scientists and analytics professionals, ensuring reusable patterns, scalable solutions and measurable business value while maintaining

Qualifications

• Senior-level leadership of AI, machine learning, data science or applied AI engineering functions.
• Strong practical understanding of machine learning and modern AI system development.
• Experience progressing AI solutions from experimentation and prototyping into production environments.
• Hands-on architectural experience with generative AI and model-enabled applications.
• Understanding of systems involving retrieval, orchestration, external tools, contextual information and human oversight.
• Experience establishing engineering standards, evaluation methodologies, monitoring practices and lifecycle controls for AI systems.
• Strong understanding of data pipelines, model development, training processes and production performance considerations.
• Experience leading multidisciplinary technical teams and developing specialist engineering capability.
• Ability to make and communicate complex technology and architecture decisions at senior organisational levels.
• Experience balancing technical quality, scalability, reliability, security, cost and business value.
• Familiarity with modern operational practices for machine-learning and AI workloads.
• Understanding of responsible AI, privacy, security and governance considerations relevant to enterprise deployment.

Responsibilities

• Set enterprise AI direction and establish reusable capabilities.
• Lead cross-functional AI engineering, data science and applied AI teams.
• Architect scalable AI systems with retrieval, orchestration and human oversight.
• Define governance, safety, monitoring and lifecycle controls for AI systems.
• Align technical strategy with business requirements and investment decisions.
• Promote responsible AI, privacy, security and compliant deployment.

Skills

AI leadership

Machine learning

Architectural design

Production AI systems

Generative AI expertise

Stakeholder management

Job description

A senior technology leader is sought to shape and expand enterprise-wide artificial intelligence capabilities within a complex organisation. The role combines strategic ownership with substantial technical involvement, covering the design, engineering, deployment and ongoing evolution of reusable AI capabilities.

The remit spans established machine learning and predictive methods through modern generative and autonomous AI approaches, alongside related areas such as optimisation, simulation, connected systems and conversational intelligence.

This position will lead a multidisciplinary group of AI and data professionals while retaining direct involvement in architecture, engineering standards, technology choices and solution quality. The successful candidate will bring broad experience across successive generations of AI technology and demonstrate the ability to turn that expertise into robust, scalable solutions with tangible organisational value.

A key objective is to establish common engineering practices, reusable capabilities, evaluation methods and operational controls that allow AI solutions to be developed and adopted consistently across different areas of the organisation.

AI Strategy & Technical Direction

• Define and maintain the organisation's roadmap for shared AI capabilities and engineering services.
• Translate strategic priorities into practical technical architectures, development approaches and delivery standards.
• Establish reusable patterns, frameworks and components that support consistent AI development.
• Provide senior technical direction on model, platform, infrastructure and system architecture decisions.

Technical Leadership & Capability Development

• Lead a multidisciplinary function comprising AI engineering, machine learning, data science and applied AI expertise.
• Develop technical capability across conventional ML, generative AI, autonomous AI systems and advanced optimisation techniques.
• Set expectations for engineering quality, technical ownership, knowledge sharing and continuous development.
• Remain sufficiently hands-on to challenge designs, guide complex technical decisions and maintain engineering standards.

Generative & Autonomous AI

• Architect scalable AI systems that combine models with retrieval, orchestration, external tools, contextual information and appropriate human intervention.
• Establish engineering practices for prompt and context design, retrieval-based systems, model evaluation, safety, monitoring and system oversight.
• Define approaches for assessing the quality, reliability and suitability of AI-generated outputs and autonomous behaviour.
• Ensure AI solutions are designed with appropriate considerations for scalability, security, resilience and operating cost.
• Provide technical oversight across model development, data preparation, feature development, training approaches and AI-system performance.
• Work with engineering teams to improve model and application quality, reliability, response times, efficiency and user outcomes.
• Diagnose complex issues affecting deployed AI solutions and drive structured remediation.
• Promote continuous technical optimisation throughout the development and production lifecycle.

Production Delivery

• Convert business and operational opportunities into sustainable AI-enabled products, services and capabilities.
• Guide the integration of AI into workflows, experiences and decision-support processes.
• Establish engineering expectations for production readiness, including security, resilience, maintainability and ongoing support.
• Ensure solutions can transition effectively from experimentation into dependable operational use.

AI Lifecycle & Operational Management

• Establish disciplined practices covering experimentation, model and application release, deployment, monitoring, evaluation and improvement.
• Introduce appropriate operational methods for managing AI and machine-learning systems throughout their lifecycle.
• Define approaches to service reliability, performance monitoring, incident response and ongoing optimisation.
• Build operational discipline around the management of increasingly complex AI systems.
• Assess emerging AI technologies and identify opportunities where they may create meaningful organisational value.
• Evaluate potential initiatives according to business impact, technical practicality, scalability and expected return.
• Set priorities across competing AI investments and development opportunities.
• Provide recommendations on technology selection, internal development versus external solutions, and allocation of technical resources.

Enterprise Stakeholder Leadership

• Work across technology, data, security, risk, governance and business functions to coordinate complex AI initiatives.
• Manage technical dependencies and competing priorities across multiple stakeholders.
• Explain architectural choices, constraints, risks and investment considerations clearly to senior decision-makers.
• Build alignment between technical strategy and practical business requirements.
• Embed responsible development principles into the design, deployment and operation of AI systems.
• Establish appropriate controls covering privacy, security, risk management and applicable compliance obligations.
• Define governance processes and readiness criteria for moving AI capabilities into operational environments.
• Balance delivery speed and experimentation with sustainable architecture, maintainability, risk management and long-term platform health.

Requirements

Experience & Technical Profile

The role requires a senior AI engineering leader with a combination of deep technical expertise and demonstrated organisational leadership.

Relevant experience should include:

• Senior-level leadership of AI, machine learning, data science or applied AI engineering functions.
• Strong practical understanding of machine learning and modern AI system development.
• Experience progressing AI solutions from experimentation and prototyping into production environments.
• Hands-on architectural experience with generative AI and model-enabled applications.
• Understanding of systems involving retrieval, orchestration, external tools, contextual information and human oversight.
• Experience establishing engineering standards, evaluation methodologies, monitoring practices and lifecycle controls for AI systems.
• Strong understanding of data pipelines, model development, training processes and production performance considerations.
• Experience leading multidisciplinary technical teams and developing specialist engineering capability.
• Ability to make and communicate complex technology and architecture decisions at senior organisational levels.
• Experience balancing technical quality, scalability, reliability, security, cost and business value.
• Familiarity with modern operational practices for machine-learning and AI workloads.
• Understanding of responsible AI, privacy, security and governance considerations relevant to enterprise deployment.

The successful candidate will be expected to operate effectively at both strategic and technical levels: setting direction for an enterprise AI capability while remaining close enough to the engineering to influence architecture, technical quality and delivery outcomes.

Strong candidates will demonstrate the ability to:

• Lead through technical credibility as well as organisational authority.
• Build reusable capabilities rather than isolated AI implementations.
• Navigate ambiguity and competing priorities in a complex environment.
• Challenge technical decisions constructively and establish clear engineering standards.
• Connect emerging AI technology with practical, measurable organisational outcomes.
• Create an environment in which specialist teams can execute with clear accountability and high technical standards.