Jobs at Syngenta Group

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 Syngenta’s scientific equipment investment programme and assigned capital projects from definition through commissioning and close-out. Responsibilities include governance, forecasting, budget and risk control, issue resolution, stakeholder and supplier coordination, project delivery, and continuous improvement of CapEx processes. The role also supports evidence-based investment decisions, scientific asset leadership, compliance, safety, and portfolio governance across the research site.

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.

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.

Develop and validate machine-learning and predictive models for Crop Protection R&D using complex scientific, laboratory, environmental, and field-trial data. Collaborate with multidisciplinary scientists and software teams, guide experimental design, support data strategy, communicate insights to technical and non-technical stakeholders, track modelling impact, document methods, and mentor junior data professionals.

Develop predictive and statistical models for Crop Protection R&D using experimental and heterogeneous datasets. Collaborate with scientific experts, support experimental design and field trials, and prototype GenAI applications including RAG, knowledge extraction, knowledge graphs, and agentic workflows. Evaluate and govern AI tools, contribute to responsible AI and data governance practices, and work with engineering teams to deploy analytical solutions.

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 AI enablement by designing and delivering scalable AI infrastructure, MLOps, model serving, vector DBs, HPC/GPU scheduling, and AI safety. Drive roadmaps, metrics, POCs, cross-team collaboration, and build/mentor a high-performing ML engineering team.

Lead design, deployment and optimization of HPC clusters, GPUs, storage, job scheduling and Kubernetes-native orchestration for scientific workloads. Partner with scientists and engineers, set standards for security, monitoring and disaster recovery, manage vendor strategy and budgets, mentor engineers, and produce documentation and training to enable reliable compute for R&D.

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.