About this role
Summary: The Lead AI TechOps Engineer drives AI transformation by designing, developing, and deploying AI solutions that bridge everyday adoption with sophisticated applications. This builder-leader role involves managing a small team of AI engineers, translating business needs into technical solutions, and ensuring scalabl...
About Us
SPH Media’s mission is to be the trusted source of news and lifestyle content in Singapore and Asia.
One of our core purposes is to produce credible, balanced, and objective news and analysis, always with a view to uphold the public good and fostering an informed, engaged citizenry.
We welcome talented individuals to join us and grow a career in a vibrant and collaborative environment built around a culture of respect and inclusivity.
As an employer, we are committed to rewarding our people fairly and developing them in their careers.
About the Role
The Head of AI TechOps plays a pivotal role in enabling and accelerating AI transformation across the organisation. Sitting within the IT Division, this role is responsible for translating business needs into practical AI-powered solutions. The Division already manages ongoing programmes that empower employees to adopt AI for “every day” use cases. The Head of AI TechOps will focus on AI use cases that require software development, system integration, workflow automation and technical orchestration beyond context engineering alone. The role leads a small, hands-on team of AI engineers, and bridges the gap between everyday AI adoption and more sophisticated AI applications, helping the organisation move from individual productivity gains to integrated, scalable and sustainable AI-enabled ways of working.
This is a builder-leader role for someone who combines technical depth, a strong product mindset and the ability to turn emerging AI capabilities into tangible organisational value.
Key Responsibilities
AI Solution Development and Delivery
- Lead the design, development, prototyping and deployment of AI solutions
- Partner with business stakeholders to identify opportunities, understand user needs, assess feasibility and translate business problems into practical AI solutions
- Prioritise and manage the AI solution development pipeline, balancing business impact, delivery effort, technical complexity and organisational readiness
- Champion rapid experimentation, prototyping and iterative development, using modern AI-assisted and vibe-coding approaches where appropriate to shorten development cycles
- Ensure prototypes can be transitioned into reliable, maintainable and supportable solutions when adopted for operational use
- Define appropriate standards for testing, deployment, documentation, version control, change management and ongoing maintenance of AI applications
AI Engineering Team Leadership
- Lead and develop a lean team of AI Engineers, providing technical direction, coaching and delivery oversight while remaining hands-on in solution architecture and complex technical problem-solving
- Foster a collaborative, agile and experimentation-driven engineering culture focused on delivering measurable business outcomes
- Establish clear development priorities, delivery practices and technical standards, while encouraging continuous learning and the adoption of emerging AI engineering capabilities
Required Qualifications
Experience and Technical Expertise
- Degree in Computer Science, Information Technology, Software Engineering, Artificial Intelligence or a related discipline, or equivalent practical experience
- Significant experience in software engineering, AI application development, platform engineering or a related technical field, including experience delivering solutions into operational use
- Strong understanding of modern AI application architectures, including large language models (LLMs), retrieval-augmented generation (RAG), AI agents, tool calling, prompt and context engineering, and workflow orchestration
- Proficiency in at least one modern programming language and the ability to design and build applications that integrate AI capabilities with enterprise systems, APIs and data sources.
- Experience with modern front-end or full-stack development, AI-assisted coding and rapid prototyping, alongside an understanding of back-end services, cloud infrastructure, deployment and operational requirements
- Familiarity with MLOps, LLMOps or DevOps practices, including CI/CD, automated testing, monitoring, version control, containerisation and production deployment
- Understanding of enterprise technology architecture, cybersecurity, identity and access management, data governance and secure software development practices
- Experience working with enterprise AI platforms, cloud services and AI development frameworks is advantageous
Leadership and Delivery Capabilities
- Demonstrated ability to lead a small, multidisciplinary engineering team while remaining hands-on in architecture, development and technical decision-making
- Strong product and problem-solving mindset, with the ability to translate ambiguous business needs into well-scoped, usable and technically sound solutions
- Comfortable balancing speed of experimentation with reliability, security, maintainability and long-term operational support
- Ability to prioritise competing demands and deliver outcomes in a fast-evolving technology environment where requirements and AI capabilities may change rapidly
- Experience working collaboratively with business stakeholders, IT teams, cybersecurity teams and other technology partners
Mindset and Personal Attributes
- Builder mentality: Enjoys turning ideas into working solutions rather than stopping at concepts, presentations or proofs of concept
- AI-native and future-oriented: Actively explores emerging AI capabilities and understands how to apply them pragmatically to solve real organisational problems
- Pragmatic innovator: Knows when a no-code solution is sufficient, when lightweight custom development is appropriate, and when a more robust engineered solution is necessary
- User- and outcome-focused: Prioritises usability, adoption and measurable business value over technical novelty
- Enabler and collaborator: Works effectively with colleagues across different levels of AI maturity and complements organisation-wide efforts to democratise AI
- Operationally minded: Understands that successful AI transformation requires not only building solutions, but also ensuring they are secure, reliable, scalable and sustainable