Lead AI/ML Engineer
A USD 119K-180K (estimate) Senior-level Full Time
Tasks
- Architect scalable ML systems
- Build platform features in Python
- Conduct performance iteration
- Deploy ML AI pipelines
- Deploy and monitor models and data
- Deploy models at scale
- Design experiments to evaluate model performance
- Design scalable platform architecture
- Develop real-time streaming solutions
- Enable MLOps
- Ensure data availability and data quality
- Implement enterprise AutoML
- Implement feature engineering
- Integrate ML models into production systems
- Manage ML/AI lifecycle
- Optimize models
- Run statistical testing
- Version models
Perks/Benefits
- N/A
Skills/Tech-stack
Automl | Azure Event | Azure Event Hubs | Azure Functions | Azure Kubernetes | Azure Kubernetes Service | BigQuery | Cloud Dataproc | Cloud Native | Cloud Native Services | Containerization | Databases | Databricks | Deep learning | DevOps | Event Hubs | Feature Engineering | Google Cloud | Google Cloud Dataproc | Google Kubernetes | Google Kubernetes Engine | Infrastructure as Code | Java | Kafka | Kubernetes | Kubernetes Engine | Kubernetes Service | MLOps | Machine Learning | Microservices | Model Deployment | Model Monitoring | Model versioning | Native services | NoSQL | PubSub | Python | Query Optimization | REST API | SQL | Snowflake | Spark Streaming | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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