AI Engineer

Lead the design, development, and deployment of production-grade AI/ML solutions that drive measurable business impact across Syngentas operations. You will own end-to-end ML pipelines, from problem definition through production deployment, while collaborating with cross-functional teams to transform agricultural and business challenges into scalable AI applications.

Accountabilities

• Design, develop, and deploy production ready AI/ML models and applications that solve critical business problems
• Own the complete ML lifecycle: data pipeline design, feature engineering, model training, evaluation, deployment, and monitoring
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions
• Implement MLOps best practices including model versioning, CI/CD pipelines, and automated retraining
• Translate complex business requirements into technical AI/ML solutions with clear success metrics
• Conduct code reviews and establish engineering best practices for AI projects
• Evaluate and integrate emerging AI technologies (LLMs, GenAI, RAG systems) into Syngentas ecosystem
• Lead POCs and MVPs using design thinking methodologies to validate solution feasibility
• Optimize model performance, latency, and cost-efficiency for production systems
• Contribute to Syngentas AI platform capabilities and reusable component libraries.

Knowledge, experience & capabilities

• 3-5 years of hands-on experience in AI/ML engineering or related roles
• Proven track record of deploying models to production environments
• Proficiency in Python
• Good understanding of AWS cloud architecture including Sagemaker and Bedrock
• Ability to use identify and re-use GitHub projects to solve business problems
• Experience working with cross-functional teams and translating business needs into technical solutions
• Demonstrated ability to manage multiple projects and deliver results in agile environments

Critical success factors & key challenges

• Strong algorithm design, analysis and reasoning skills
• Ability to deliver POCs, MVPs, Experiments, technology evaluations following design thinking practices
• Ability to orchestrate efforts needed to prioritize business initiatives across complex change agendas
• Excellent communication and stakeholder management skills to explain technical information to individuals who dont have the same technical background
• Problem solving and decision-making skills
• Risk assessment and mitigation for AI/ML projects
• Proactive in identifying opportunities for AI-driven improvements
• Teamwork, team management and leadership skills

Innovations

Employee may, as part of his/her role and maybe through multifunctional teams, participate in the creation and design of innovative solutions. In this context, Employee may contribute to inventions, designs, other work product, including know-how, copyrights, software, innovations, solutions, and other intellectual assets.

Degree in Computer Science, AI, ML, Data Science, Engineering, or related fields.