R&D Analytics & AI Engineer — Production ML Solutions

Syngenta Group; Netherlands; Hybrid; EUR 90.000 - 150.000; Vollzeit

Zusammenfassung

Syngenta Group is seeking an R&D Analytics - AI Developer to build AI-powered software for the Vegetable R&D team. You will design data pipelines, integrate models, and deliver scalable analytics in a cloud environment.

The role requires deep Python expertise, experience with AI frameworks, and a strong foundation in software engineering, data governance, and collaboration with scientists.

Qualifikationen

• Master or PhD in Data Science, AI, CS, Quantitative Genetics, or a related field.
• 5+ years of professional software engineering experience with at least 3 years building/maintaining production Python systems.
• Familiarity with AI frameworks.
• Strong knowledge of Agentic AI and large language models.
• Experience packaging, deploying, and monitoring ML models in production.

Aufgaben

• Design and build data pipelines to support ML models and analytics.
• Build analytical software that integrates R&D data, models, and decision frameworks.
• Containerization and orchestration within a cloud environment.
• Managing software deployment in non-production and production environments.
• Partner with R&D scientists and analytics teams to translate needs into scalable software solutions.
• Use AI-assisted software development tools to improve productivity, code quality, and testing.
• Evaluate emerging AI capabilities to improve analytics, experimentation, and decision support.
• Ensure solutions are scientifically valid, explainable where needed, and compliant with governance.

Kenntnisse

Python programming

Big data platforms

Cloud-native apps

Agentic AI

AI frameworks

Software engineering

Ausbildung

Master or PhD in Data Science, AI, CS, Quantitative Genetics

Tools

Snowflake

Databricks

RESTful APIs

GraphQL

Docker

Kubernetes

Jobbeschreibung

Syngenta Group is seeking an R&D Analytics - AI Developer to build AI-powered software for the Vegetable R&D team. You will design data pipelines, integrate models, and deliver scalable analytics in a cloud environment.

The role requires deep Python expertise, experience with AI frameworks, and a strong foundation in software engineering, data governance, and collaboration with scientists.