Company Description
Join Syngenta Group, a leader in agricultural innovation where technology meets purpose. As digital pioneers in AgTech, we're integrating AI across our value chain from smart breeding to precision agriculture. Our global team of 56,000 professionals is transforming sustainable farming worldwide. At Syngenta IT & Digital, your expertise will directly impact food security and shape the future of agriculture through cutting-edge technology.
Website address - https://www.syngentagroup.com/
Job Description
Join our Data Science Platform team as an MLOps Engineer, where you'll build and maintain production ML infrastructure across AWS SageMaker and Databricks. You'll enable data scientists to efficiently develop, deploy, and monitor ML models at scale while establishing governance and best practices for our multiplatform ML ecosystem.
What You'll Do
- ML Infrastructure & Operations
- Build and maintain end-to-end ML pipelines for training, deployment, and monitoring across AWS SageMaker and Databricks
- Create Feature Stores using Feature Engineering and EDA
- Implement MLflow for experiment tracking, model versioning, and registry management across platforms
- Develop automated CI/CD pipelines for model deployment using Jenkins/GitHub Actions/GitLab CI
- Create reusable Python libraries and Terraform modules for standardized ML operations
Feature Engineering & Management
- Develop feature pipelines using Databricks Feature Store and SageMaker Feature Store
- Implement feature versioning, lineage tracking, and governance through Unity Catalog
- Build feature serving infrastructure for online and offline access
- Ensure feature discoverability and reusability across ML projects
ML Governance & Monitoring
- Leverage Unity Catalog for ML model governance, access control, and lineage tracking
- Implement Databricks Lakehouse Monitoring and SageMaker Model Monitor for drift detection
- Build dashboards and alerting for model performance, data quality, and prediction monitoring
- Deploy ML explainability frameworks (SHAP, LIME) for model interpretability
Platform Interoperability
- Design cross-platform ML workflows ensuring seamless integration between AWS, Databricks etc
- Implement deployment strategies: A/B testing, canary deployments, bluegreen rollouts
- Optimize distributed training and hyperparameter tuning (Ray Tune, Optuna, SageMaker HPO)
- Collaborate with data scientists to productionize models and establish best practices
What You Bring
Required Experience
- 4-7 years in ML engineering, MLOps, or data science engineering
- 1-2+ years hands on with Databricks and/or AWS SageMaker in production
- Proven track record deploying and maintaining production ML models at scale
Technical Skills
- ML Platforms: Databricks (Workflows, Jobs, Delta Lake, Unity Catalog), AWS SageMaker (Pipelines, Training, Endpoints, Feature Store)
- MLOps: MLflow (tracking, registry, deployment), model monitoring, drift detection, deployment strategies
- Programming: Strong Python (pandas, scikitlearn, PyTorch/TensorFlow, XGBoost), PySpark, SQL
- Infrastructure: Terraform (modular code, Databricks/AWS providers), Docker, Git
- Cloud: AWS (S3, Lambda, ECR, IAM, CloudWatch), distributed training frameworks
- CI/CD: Jenkins/GitHub Actions/GitLab CI, automated testing, deployment automation
ML & Data Science Knowledge
- ML algorithms, model evaluation, validation techniques, and statistical testing
- Feature engineering, hyperparameter optimization, and model optimization
- Understanding of model drift, retraining strategies, and ML explainability
- Experience with Feature Stores and feature governance
Qualifications
- Certifications: Databricks ML Professional, AWS ML Specialty, Terraform Associate
- Experience with distributed training (Ray, Horovod), Kubernetes, AutoML
- Contributions to opensource ML/MLOps projects
- Knowledge of model risk management practices
Additional Information
Note: Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity, marital or veteran status, disability, or any other legally protected status.
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