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ML Ops Engineer
- Concord, ON, Canada
Contract
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Job details
- Job type
- Contract
- Closing date
- Posting language
- English
- Posted
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Archived listing. The details below describe a past opening.
Position: ML Ops Engineer
Location : Concord, CA
Duration: Contract
Job Description::
- Develop predictive models using structured/unstructured data across 10+ business lines, driving fraud reduction, operational efficiency, and customer insights.
- Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment
- Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.
- Automate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS, Azure).
- Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.
- Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)
- Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs
- Strong proficiency in Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
- Experience with cloud platforms and containerization (Docker, Kubernetes).
- Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.
- Solid understanding of software engineering principles and DevOps practices.
- Ability to communicate complex technical concepts to non-technical stakeholders.
More about this job
Responsibilities
Develop and deploy predictive models and ML pipelines to drive fraud reduction and operational efficiency. Automate model training, monitoring, and lifecycle management within cloud environments using CI/CD workflows.
Requirements
Requires strong proficiency in Python, SQL, and major ML libraries along with experience in cloud platforms and containerization. Candidates should be familiar with data engineering tools and software engineering principles.
Skills
- Python
- SQL
- ML Ops
- Vertex AI
- MLflow
- Kubeflow
- Docker
- Kubernetes
- Scikit-learn
- XGBoost
- TensorFlow
- PyTorch
- CI/CD
- Airflow
- Spark
- AutoML
Visa sponsorship
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Categories
- Data & Analytics
- Technology
- Software
- Engineering
- Consulting
Keywords
- ML Ops
- Machine Learning
- Vertex AI
- AutoML
- H2O Driverless AI
- MLflow
- Kubeflow
- GCP
- AWS
- Azure
- CI/CD
- Docker
- Kubernetes
- Python
- SQL
- Scikit-learn
- XGBoost
- TensorFlow
- PyTorch
- Airflow
- Spark
- Model Governance
- MRM
- API
- Predictive Modeling
- Data Engineering
- DevOps
- Containerization
- Observability
- Cloud Computing