NavitasPartners - 456 emplois
Toronto, ON
Posté aujourd'hui
Détails de l'emploi :
Temps plein
Expérimenté
Job Title: MLOps Engineer
Position Overview:
The MLOps Engineer will establish scalable machine learning operations frameworks and automate the deployment, monitoring, and governance of AI models.
Key Responsibilities:
- Build ML deployment pipelines.
- Implement CI/CD for machine learning workloads.
- Automate model retraining and validation.
- Establish model monitoring and observability.
- Manage ML infrastructure and environments.
- Ensure compliance and governance requirements.
Required Skills:
- MLOps
- MLflow
- Kubeflow
- Docker
- Kubernetes
- Python
- GitHub Actions
- Azure DevOps
- Terraform
- Monitoring Tools
Required Qualifications:
- Bachelor's degree in Computer Science or related field.
- 5+ years of MLOps or ML Engineering experience.
Mandatory Industry Experience:
- Must have prior BFSI experience supporting regulated AI/ML environments, model governance, risk management, fraud analytics, or financial decisioning systems.
For more details reach at [email protected]
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Foire aux questions
The role involves building ML deployment pipelines, implementing CI/CD for ML workloads, automating model retraining and validation, establishing model monitoring and observability, managing ML infrastructure and environments, and ensuring compliance and governance.
Required skills include MLOps, MLflow, Kubeflow, Docker, Kubernetes, Python, GitHub Actions, Azure DevOps, Terraform, and Monitoring Tools.
A bachelor's degree in Computer Science or a related field and 5+ years of MLOps or ML Engineering experience are required.
Yes, the role requires mandatory BFSI experience supporting regulated AI/ML environments, model governance, risk management, fraud analytics, or financial decisioning systems.
This is a full-time on-site role in Toronto, ON with NavitasPartners.
The role emphasizes deployment pipelines, CI/CD for ML workloads, automated retraining and validation, and governance and compliance of AI models.
