AI Engineer Python

We are looking for a hands-on, senior-level Software Engineer with deep Python expertise to build reliable internal platforms and developer-enablement capabilities. This is an engineering-first role: strong software design, coding fluency and practical problem-solving are essential.

You will apply AI in pragmatic ways-such as designing document search and conversational experiences using Retrieval-Augmented Generation (RAG)-while maintaining a high bar for engineering quality, security, scalability and operational excellence.

What You’ll Do

• Design, build and maintain production-grade Python services, libraries and developer tools.
• Apply modern Python practices, including packaging, type hints, asynchronous programming and maintainable API design.
• Create shared libraries, service templates and engineering standards that enable multiple internal teams to build consistently and efficiently.
• Build and evolve AI-enabled capabilities, including RAG-based search and conversational solutions over internal documentation and knowledge sources.
• Define robust evaluation approaches for AI-assisted features, balancing usefulness, reliability, security and cost.
• Develop and maintain CI/CD pipelines and cloud-native delivery practices using Docker, Kubernetes and Terraform.
• Establish strong testing, code quality, documentation-as-code and secure software development practices.
• Improve application performance, scalability and reliability through profiling, benchmarking, observability and architectural improvements.
• Implement monitoring, alerting and operational standards for platform services.
• Conduct thorough code reviews, mentor engineers and raise engineering standards across the team.
• Partner with engineering, product and scientific stakeholders to translate complex needs into practical, scalable technical solutions.

Required Qualifications

• Significant hands-on experience as a Software Engineer, with strong, production-level proficiency in Python. Equivalent depth in Java may also be considered.
• Demonstrated ability to write clean, maintainable and well-tested code, and to solve complex engineering problems independently.
• Strong experience with modern Python practices: packaging, dependency management, typing, async programming and service/API development.
• Experience designing and maintaining reusable libraries, service templates or internal developer platforms used by multiple teams.
• Strong knowledge of software quality practices, including automated testing, code reviews, CI/CD and release engineering.
• Practical experience with Docker, Kubernetes and Infrastructure as Code, ideally Terraform.
• Experience with observability, monitoring, alerting, performance profiling and benchmarking.
• Solid understanding of secure coding practices, vulnerability management and security considerations in software delivery.
• Applied understanding of Generative AI and LLM patterns, particularly RAG. You should be able to explain and design an approach for document search or conversational interfaces over enterprise content.
• Strong written and spoken English, with the ability to communicate effectively across technical and non-technical stakeholders.

Preferred Qualifications

• Experience building internal platforms, developer tools or products adopted by several engineering teams.
• Experience in agriculture, life sciences, scientific computing or another regulated enterprise environment.
• Experience working in distributed, international engineering organisations.
• Track record of technical leadership, mentoring or leading cross-team engineering initiatives.
• Familiarity with AI/ML frameworks and tools used in software development environments.

What Success Looks Like

You are an engineer first: someone who can go deep in code, make sound technical decisions and deliver durable software in production. You use AI thoughtfully and practically-not as a substitute for engineering fundamentals, but as a capability embedded in reliable, secure and scalable platforms.

Why Syngenta?

• Meaningful impact: help build technology that supports scientific innovation and sustainable agriculture.

• Complex, modern challenges: work on high-scale platforms and systems with room for technical judgement.

• Collaborative environment: partner with global engineering, R&D and product communities.

• Growth: broaden your influence through challenging work, visible outcomes and a global network