Responsibilities
Design and build data pipelines supporting machine learning model development and advanced analytics.
Develop analytical software and AI solutions integrating R&D-specific; data, models, decision frameworks, and workflows.
Package, deploy, monitor, and maintain machine learning models and software in production.
Build containerized and orchestrated applications in cloud environments, especially AWS.
Manage software deployment and configuration across non-production and production environments.
Partner with R&D; scientists, data scientists, and analytics teams to deliver scalable software solutions.
Use AI-assisted development tools to improve productivity, code quality, documentation, and testing while retaining technical accountability.
Evaluate agentic AI and automation capabilities for R&D; analytics, experimentation, and decision support.
Ensure solutions are scientifically valid, explainable where needed, reliable, and aligned with governance, security, and compliance expectations.
Requirements
Master's or PhD in Data Science, Artificial Intelligence, Computer Science, Quantitative Genetics, or a related field.
At least 5 years of professional software engineering experience,



including at least 3 years building and maintaining production systems in Python.
Strong Python programming and software engineering skills with experience designing, building, and maintaining end-to-end software solutions.
Familiarity with artificial intelligence and machine learning frameworks, agentic AI, and large language models.
Experience with big data platforms such as Snowflake and Databricks.
Experience packaging, deploying, and monitoring machine learning models in production.
Proficiency developing cloud-native applications and deploying them to cloud environments, especially AWS.
Experience with API technologies such as RESTful APIs or GraphQL.
Experience developing software products or analytical applications for scientific, R&D;, agricultural, biological, or similarly complex data domains.
Experience using AI coding assistants, enterprise AI platforms, or agentic AI tools with independent technical review, testing, and validation.
Familiarity with responsible AI practices including model evaluation, bias and error assessment, traceability, prompt and version management, and human-in-the-loop workflows.
Benefits
Work from any Syngenta site in Mexico under the local hybrid-work model.

📌 R&D Analytics - Ai Developer (Xico)
🏢 Syngenta Group
📍 Xico