Lead AI Enablement Technology

Syngenta Crop Protection is a leader in agricultural innovation, bringing breakthrough technologies and solutions that enable farmers to grow productively and sustainably. We offer a leading portfolio of crop protection solutions for plant and soil health, as well as digital solutions that transform the decision-making capabilities of farmers. Our 17,900 employees serve to advance agriculture in more than 90 countries around the world.

Syngenta Crop Protection is headquartered in Basel, Switzerland, and is part of the Syngenta Group.

Our employees reflect the diversity of our customers, the markets where we operate and the communities which we serve. No matter what your position, you will have a vital role in safely feeding the world and taking care of our planet. Join us and help shape the future of agriculture.

About the role

We are seeking a Lead, AI Enablement Technologies to run a cross-cutting strategic function within Engineering and Technology. This role makes AI a first-class, measured, and safely operated capability across R&D IT. You will own the engineering "how" of AI: infrastructure, technology choices, engineering practice, and the platform that business-facing AI products run on.

What you will do

Own the operational performance of the AI Enablement function across five key workloads: HPC and scientific compute, AI inference and serving infrastructure, agentic foundations, AI-assisted engineering, and AI upskilling for R&D.

Direct line management for area leads, guiding specialists in HPC, scientific AI inference, and engineering AI.

Drive the technology roadmap for AI infrastructure across R&D IT, including model serving stacks, inference optimisation patterns, guardrails frameworks, vector database choices, and evaluation harnesses.

Manage HPC investments, including GPU procurement and cluster lifecycle, grounded in utilisation data and scientific demand forecasts.

Partner with peer data, analytics, and bioinformatics IT functions to define API contracts, cost attribution models, and shared scientific-computing patterns.

Own the evaluation and safety framework for AI at scale, including red-teaming, prompt-injection testing, and model performance tracking.

Build consensus on cross-boundary technical patterns and author Architecture Decision Records (ADRs).

Establish and monitor key performance indicators (KPIs) for infrastructure utilisation, cost efficiency, and system reliability, ensuring accountability across the AI Enablement function.

Develop and deliver comprehensive capability-building programmes for R&D teams, including training on AI best practices, platform usage, and responsible AI principles.

Manage vendor relationships and technology partnerships to ensure optimal selection, integration, and support of AI infrastructure components and tools.

What you will bring

Expert-level technical depth in AI infrastructure and MLOps, including model serving at scale, inference optimization, vector databases, and LLM-adjacent tooling.

Strong scientific HPC literacy (e.g., Slurm, GPU scheduling, scientific-computing workflows) to credibly direct HPC specialists and engage with scientists.

Proven engineering leadership experience running specialist teams and hiring senior/staff engineers.

Strong strategic navigation skills to partner effectively across boundaries without being captured by vendors or peer functions.

Hands-on habits; you still read code, review architecture, and evaluate tools directly.

Exceptional communication skills, able to explain complex AI infrastructure to executives and engineers alike.

Demonstrated experience designing and implementing safety frameworks, including red-teaming methodologies, adversarial testing, and model performance evaluation at scale.

Proven track record in managing technology budgets, capital expenditure planning, and cost optimization across infrastructure investments.

Experience authoring and socializing technical architecture decisions and design documentation across cross-functional teams.

Strong vendor management and technology partnership experience, with ability to evaluate, negotiate, and integrate third‑party solutions effectively.

Hands-on experience with popular ML frameworks, tools, and platforms (e.g., PyTorch, Tensor Flow, Kubernetes, Ray, or equivalent) to maintain technical credibility and inform infrastructure decisions.

Demonstrated success leading large-scale technology initiatives or organizational transformations, with ability to…