Jobs at Syngenta Group

Leads AI enablement across R&D IT, overseeing HPC, AI inference, agentic foundations, engineering AI, and upskilling. Owns infrastructure strategy, model serving, safety and evaluation frameworks, GPU procurement, vendor partnerships, architecture decisions, cost optimization, KPIs, and specialist engineering teams. Builds scalable, reliable, and responsibly operated AI capabilities while driving adoption and technical alignment across scientific and technology functions.

Owns and evolves HPC infrastructure supporting scientific computing and AI/ML workloads. Responsibilities include cluster architecture, GPU provisioning, storage, scheduling, orchestration, capacity planning, vendor management, procurement, performance optimization, security, disaster recovery, documentation, budgeting, and executive communication. Leads and mentors engineering teams while partnering with scientists, computational specialists, and bioinformaticians to deliver reliable, scalable infrastructure.

Develop predictive models, machine-learning, AI, and Generative AI solutions for Crop Protection R&D. Analyze complex scientific datasets, identify valuable AI use cases, validate and govern models, and deploy scalable analytical applications. Partner with scientists, software teams, and stakeholders to support product discovery and decision-making. Communicate insights, track performance metrics, document methodologies, and mentor junior data scientists.

Leads the R&D Activity Product-Oriented Delivery portfolio, setting product vision, roadmaps, priorities, investment decisions, and success metrics. Oversees digital products supporting laboratory and field R&D, including experimentation, trials, sensors, imaging, and geospatial data. Leads cross-functional stakeholders and Product Managers while managing delivery governance, data governance, security, compliance, vendors, budgets, risks, and operational excellence. Ensures scalable, secure, high-quality products that deliver measurable business and user outcomes.

Build production-grade Python services, reusable libraries, developer platforms, and AI-enabled RAG search and conversational solutions. Develop CI/CD and cloud-native delivery practices with Docker, Kubernetes, and Terraform. Establish testing, security, observability, monitoring, and operational standards while improving scalability and performance. Partner with technical and scientific stakeholders, conduct code reviews, mentor engineers, and lead engineering improvements across teams.

Lead the technical direction of AWS and GCP cloud foundations, including Terraform, infrastructure as code, networking, governance, security, FinOps, reliability, and incident response. Partner with engineering, product, and scientific teams; conduct architecture reviews; establish standards; guide cloud design decisions; and mentor engineers. The role also requires stakeholder management with hyperscalers, technical leadership without direct authority, and rigorous use of AI-assisted engineering.

Lead design and implementation of AI-powered decision support and planning systems for R&D. Drive backend engineering (Python/Java), architecture, document ingestion, embeddings-based retrieval, testing, security, and scalability. Mentor engineers, collaborate across product and science teams, and maintain technical documentation and compliance.

Lead platform and infrastructure initiatives, manage engineering teams and vendors, own incident/SLA processes, delivery across workstreams, budget and SoW oversight, define KPIs, drive modernization and AI-assisted engineering practices, and communicate operational status to stakeholders.

Lead design and delivery of scalable backend and frontend solutions for screening operations and logistics. Own end-to-end systems, apply AI-assisted engineering, mentor junior engineers, drive CI/CD and DevOps practices, optimize performance, and collaborate with product and scientific teams to translate complex workflows into reliable production services.

Analyze historical biological and field-trial data to uncover patterns affecting product performance, build and validate ML/deep learning models for crop protection outcomes, support R&D decision-making, design field trials with technical managers, integrate models with IT/software, and communicate insights to technical and non-technical stakeholders.