Key Responsibilities
- Develop and deploy:
- Recommendation models
- Ranking models
- Customer segmentation models
- Propensity models
- Personalization services
- Build real-time recommendation systems leveraging streaming customer events.
- Create large-scale batch processing pipelines for customer behavior analysis and model training.
- Engineer and manage feature pipelines for production ML systems.
- Implement MLOps best practices including CI/CD, monitoring, retraining, testing, and governance.
- Partner with product managers, data scientists, and engineers to launch new personalization capabilities.
- Conduct A/B testing and experimentation to optimize customer outcomes.
- Deploy models into production environments and monitor model performance.
- Support cloud modernization initiatives and migration of ML workloads.
Required Technology Stack
- Python
- Spark
- Databricks
- Kafka
- TensorFlow and/or PyTorch
- MLOps
- Model Deployment
- Feature Engineering
Preferred Cloud Experience
- AWS
- BigQuery
- Vertex AI
- Pub/Sub
- Dataflow
- GKE
- Cloud Run
- AWS $B"*(B Google Cloud Platform migration experience
Target Background
- 4-8 years of experience
- ML Engineering, MLOps, or Data Platform Engineering
- Recommendation systems or personalization experience
- Retail/eCommerce customer analytics preferred
Rate:
Not specified
Location:
Remote
IR35 Status:
Not specified
Remote Status:
Remote
Industry:
AI & Machine Learning
Seniority Level:
Mid-Level