Machine Learning Engineer
Tasks
- Build and maintain MLOps infrastructure
- Collaborate to productionize optimize and scale ML solutions
- Containerize ML workloads with Docker and deploy scalable services
- Develop automated training validation testing deployment pipelines
- Develop infrastructure as code for AI platforms and cloud resources
- Evaluate and implement MLOps tools frameworks and best practices for LLM and agentic AI
- Implement experiment tracking model versioning and model registry
- Manage model lifecycle retraining rollback reproducibility governance auditability
- Monitor production ML systems model performance data quality drift and latency
- Orchestrate workflows feature engineering and data processing pipelines
Perks/Benefits
- Employee stock-option
- Flexible paid time off
- Hybrid work options
- Paid family leave
- Remote work up to 30 days
- Team activity budget
- Work-life balance
Skills/Tech-stack
AWS | Amazon SageMaker | Artifact management | Azure | CI/CD | Cloud platform | CloudFormation | DBT | Data Drift | Data Drift Detection | Data Quality | Data quality monitoring | Docker | Drift Detection | Experiment tracking | Feature Engineering | Git | Google Cloud | Google Cloud Platform | Infrastructure as Code | Kubernetes | MLflow | Model Monitoring | Model Registry | Model versioning | Observability | Python | Quality monitoring | SQL | Snowflake | Snowpark ML | Terraform | Vertex AI | Workflow Orchestration | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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