MLOps Engineer ML Platforms

Join our Data Science Platform team as an MLOps Engineer where youll build and maintain production ML infrastructure across AWS SageMaker and Databricks. Youll 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 Youll 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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Employment Type :

Full-time