R&D AI & Analytics Engineer — Hybrid, Python

Syngenta Group

North Carolina

Hybrid

USD 105,000 - 157,000

Full time

5 days ago

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Benefits offered by this job

Flexible work options

Full benefits package (Medical, Dental

401k plan with company match

Paid vacation and holidays

Job summary

Syngenta Vegetable Seeds seeks an R&D Analytics - AI Developer in Durham, NC to design data pipelines and AI-powered analytics for vegetable R&D. You will translate scientific needs into scalable software and ensure reliable production deployments across cloud environments.

The role emphasizes agentic AI, responsible AI practices, and collaboration with data scientists to accelerate delivery using modern AI platforms.

Qualifications

Master or PhD in Data Science, Artificial Intelligence, Computer Science, Quantitative Genetics, or a related field
Minimum of 5 years of professional software engineering, with at least 3 years building and maintaining production systems in Python
Familiarity with AI and machine learning frameworks
Strong knowledge and experience of Agentic AI and large language models
Proven experience designing, building, and maintaining end-to-end software solutions
Strong Python programming and software engineering skills
Experience with big data platforms and technologies (Snowflake, Databricks)
Experience packaging, deploying, and monitoring ML models in production
Proficiency in developing cloud-native applications and deploying to AWS
Experience with API technologies (RESTful APIs or GraphQL)
Experience developing software products or analytical applications for scientific, R&D, agricultural, biological, or similarly complex data domains
Demonstrated ability to use AI coding assistants, enterprise AI platforms, or agentic AI tools to accelerate software delivery while applying independent technical review, testing, and validation
Familiarity with responsible AI practices, including model evaluation, bias/error assessment, traceability, prompt/version management, and human-in-the-loop workflows

Responsibilities

Design and build data pipelines to support the development and deployment of machine learning models and advanced analytics
Build analytical software and AI solutions that integrate R&D-specific data, models, and decision frameworks into intuitive user workflows
Build application containerization and orchestration within a cloud environment
Managing software deployment and configuration in non-production and production environments
Partner with R&D scientists, data scientists, and analytics teams to translate scientific and operational needs into scalable software solutions
Use AI-assisted software development tools to improve productivity, code quality, documentation, testing, while maintaining strong engineering judgment and accountability for final outputs
Evaluate emerging AI, agentic AI, and automation capabilities and identify where they can responsibly improve R&D analytics, experimentation, and decision support
Ensure solutions are scientifically valid, explainable where needed, reliable in production, and aligned with data governance, model governance, security, and compliance expectations