Artificial Intelligence Engineer

We are seeking a Principal AI Engineer with exceptional technical depth and leadership capability to shape and accelerate our artificial intelligence agenda. This role is suited to a technical visionary who combines hands-on expertise across AI, machine learning, and software engineering with the ability to lead teams, influence architecture, and define long-term technical strategy. The successful candidate will play a pivotal role in architecting next-generation agentic AI solutions, setting technical direction, and establishing next practice for AI development across the organisation. This is a high-impact, senior technical leadership role with responsibility not only for delivery, but for elevating standards, capability, and future potential of AI engineering.

Key Responsibilities

• Technical Leadership & Architecture

• Define and execute the organisation’s agentic AI strategy in partnership with senior technology leadership.
• Lead the design of scalable, resilient AI and data architectures aligned to business objectives.
• Establish technical standards, architectural principles, and development methodologies for AI/ML initiatives.
• Lead the end-to-end development of advanced AI/ML systems, from research and prototyping through to production deployment.
• Drive innovation across machine learning, deep learning, and emerging AI capabilities including generative, multi-modal, and agent-based systems.
• Oversee the implementation of MLOps practices, including model lifecycle management, monitoring, and automated deployment.
• Lead and mentor senior AI engineers, data scientists, and data engineers.
• Build a culture of technical excellence, continuous learning, and innovation within the AI engineering function.
• Collaborate closely with other engineering, data, and product teams to deliver high-impact AI solutions.

• Stay at the forefront of AI research, tools, and techniques, assessing and integrating relevant advances into the technology stack.
• Drive proof-of-concepts and experimental initiatives to explore new AI capabilities and applications.
• Contribute to the broader AI community through open-source work, thought leadership, and participation in technical forums or conferences.

• Establish robust testing, performance monitoring, and quality assurance frameworks for AI systems.
• Ensure solutions meet enterprise standards for reliability, scalability, security, and compliance.
• Lead technical design reviews and provide guidance on complex AI engineering challenges.

• Deep hands-on experience with AI/ML frameworks such as TensorFlow, PyTorch, Scikit-Learn, and Transformer-based models.
• Strong knowledge of cloud platforms (AWS, Azure, or GCP) and modern data and AI architectures.
• Expertise in at least one advanced domain such as deep learning, NLP, computer vision, or reinforcement learning.
• Proven experience building production-grade AI systems with a focus on performance, reliability, and maintainability.
• Strong grounding in software architecture, distributed systems, and DevOps / MLOps practices.
• Demonstrated ability to lead senior technical teams and influence architectural decisions at scale.
• Strong mentoring capability with a track record of developing high-performing engineers.
• Comfortable operating across organisational boundaries to drive alignment and delivery.

• Track record of solving complex, ambiguous technical problems and delivering AI solutions that create measurable business value.

Qualifications & Experience

• Significant experience in AI / ML engineering, including at least two years in a senior or principal-level technical leadership role.
• Master’s degree or higher in AI, Data Science, Computer Science, or a related field requiring strong statistical rigour (PhD advantageous).
• Proven experience leading the design, build, and deployment of AI systems at scale.
• Evidence of thought leadership, publications, or meaningful contributions to open-source AI projects is highly desirable.
• Strong familiarity with modern software development practices including CI/CD, testing frameworks, and agile delivery.