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SeniorFull-timePythonAWSAgileCI/CDGitAIML
Salary Range
$26,520 - $35,880 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
We are seeking an AI/ML Engineer to lead the design, development, and deployment of production-grade AI/ML solutions at Syngenta. This role involves owning end-to-end ML pipelines and collaborating with cross-functional teams to transform agricultural and business challenges into scalable AI applications.
Key Responsibilities
- →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 Syngenta's 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 Syngenta's AI platform capabilities and reusable component libraries.
Requirements
- ✓3+ 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 identify and reuse 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
- ✓Strong algorithm design, analysis, and reasoning skills
- ✓Ability to deliver POCs, MVPs, experiments, and technology evaluations following design thinking practices
- ✓Excellent communication and stakeholder management skills to explain technical information to non-technical individuals
- ✓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.
