Lead AI Enablement Technology

**What You'll Do** - Lead and apply deep expertise in AI infrastructure and MLOps to deliver durable, scalable outcomes. - Lead and apply deep expertise in model serving and inference optimisation to deliver durable, scalable outcomes. - Lead and apply deep expertise in vector databases and LLM-adjacent tooling to deliver durable, scalable outcomes. - Lead and apply deep expertise in scientific HPC and GPU scheduling literacy to deliver durable, scalable outcomes. - Lead and apply deep expertise in AI safety, evaluation and guardrails to deliver durable, scalable outcomes. - Lead and apply deep expertise in engineering leadership of specialists to deliver durable, scalable outcomes. - Collaborate across engineering, product and scientific teams to translate complex needs into practical solutions. - Set a high bar for quality, documentation and thoughtful use of AI-assisted engineering. - Develop and communicate a strategic roadmap for AI enablement technologies that aligns with organisational objectives and scientific innovation goals. - Establish performance metrics, monitoring frameworks and evaluation criteria to measure the effectiveness and impact of AI infrastructure investments. - Build and mentor a high-performing team of AI and ML specialists, fostering a culture of continuous learning and technical excellence. - Evaluate emerging AI technologies and tools, conducting proof-of-concept initiatives to assess suitability for R&D workflows and agricultural applications. - Manage stakeholder relationships with scientific teams, product leaders and IT leadership to ensure alignment on priorities, resource allocation and capability delivery. - Champion best practices in responsible AI deployment, including bias detection, model transparency and compliance with organisational governance standards. **What You Bring** - Demonstrable experience in AI infrastructure and MLOps. - Demonstrable experience in model serving and inference optimisation. - Demonstrable experience in vector databases and LLM-adjacent tooling. - Demonstrable experience in scientific HPC and GPU scheduling literacy. - Demonstrable experience in AI safety, evaluation and guardrails. - Demonstrable experience in engineering leadership of specialists. - Demonstrable experience in architecture decision records and technology roadmaps. - Proven ability to develop and communicate strategic technical roadmaps aligned with organisational objectives. - Strong stakeholder management skills with the ability to influence across technical and non-technical audiences. - Experience establishing performance metrics, monitoring frameworks and evaluation criteria for infrastructure investments. - Demonstrated capability in building, mentoring and developing high-performing technical teams. - Experience conducting proof-of-concept initiatives and evaluating emerging technologies for suitability and impact. - Strong communication skills and the ability to influence across technical and non-technical stakeholders. - Knowledge of responsible AI principles, including bias detection, model transparency and governance compliance. - Fluent English, written and spoken. **Bonus Points** - Experience in agriculture, life sciences, scientific computing or a regulated enterprise environment. - Experience building platforms, products or services used by multiple internal teams. - Experience working in distributed, international engineering organisations.