To ensure the seamless deployment monitoring automation and lifecycle management of AI/ML systems across the enterprise. The role ensures robust MLOps and AIOps capabilities supports Data Scientists in operationalizing models manages production workloads including LLMs RAG pipelines and predictive models and ensures compliance with governance and regulatory requirements.
- Support execution of the banks Data Science & AI strategy through reliable ML/AI operationalization.
- Design build and maintain end-to-end MLOps/AIOps pipelines for scalable deployment and monitoring.
- Manage CI/CD pipelines for model deployment automated testing and infrastructure-as-code.
- Automate retraining model evaluation and drift detection processes.
- Monitor model performance stability latency and data quality across production systems.
- Implement observability tools for logs metrics explainability and failure recovery.
Qualifications :
- Bachelors Degree in Computer Science Engineering Data Science or related fields.
- Masters Degree or certifications in Azure ML AWS ML Kubernetes MLOps DevOps or Cloud Engineering can be an advantage.
Experience
- 46 years in Machine Learning Engineering MLOps AIOps or related roles.
- Experience supporting Data Scientists with model operationalization and deployment.
- Strong background managing production AI systems including LLMs RAG pipelines and predictive models.
- Experience with Azure Kubernetes Docker MLflow Databricks CI/CD and model monitoring tools.
Additional Information :
Behavioural Competencies:
- Adopting Practical Approaches
- Articulating Information
- Examining Information
- Exploring Possibilities
- Providing Insights
- Team Working
Technical Competencies:
- Data Analysis
- Database Administration
- Data Integrity
- Knowledge Classification
- Research & Information Gathering
Remote Work :
No
Employment Type :
Full-time
To ensure the seamless deployment monitoring automation and lifecycle management of AI/ML systems across the enterprise. The role ensures robust MLOps and AIOps capabilities supports Data Scientists in operationalizing models manages production workloads including LLMs RAG pipelines and predictive m...
To ensure the seamless deployment monitoring automation and lifecycle management of AI/ML systems across the enterprise. The role ensures robust MLOps and AIOps capabilities supports Data Scientists in operationalizing models manages production workloads including LLMs RAG pipelines and predictive models and ensures compliance with governance and regulatory requirements.
- Support execution of the banks Data Science & AI strategy through reliable ML/AI operationalization.
- Design build and maintain end-to-end MLOps/AIOps pipelines for scalable deployment and monitoring.
- Manage CI/CD pipelines for model deployment automated testing and infrastructure-as-code.
- Automate retraining model evaluation and drift detection processes.
- Monitor model performance stability latency and data quality across production systems.
- Implement observability tools for logs metrics explainability and failure recovery.
Qualifications :
- Bachelors Degree in Computer Science Engineering Data Science or related fields.
- Masters Degree or certifications in Azure ML AWS ML Kubernetes MLOps DevOps or Cloud Engineering can be an advantage.
Experience
- 46 years in Machine Learning Engineering MLOps AIOps or related roles.
- Experience supporting Data Scientists with model operationalization and deployment.
- Strong background managing production AI systems including LLMs RAG pipelines and predictive models.
- Experience with Azure Kubernetes Docker MLflow Databricks CI/CD and model monitoring tools.
Additional Information :
Behavioural Competencies:
- Adopting Practical Approaches
- Articulating Information
- Examining Information
- Exploring Possibilities
- Providing Insights
- Team Working
Technical Competencies:
- Data Analysis
- Database Administration
- Data Integrity
- Knowledge Classification
- Research & Information Gathering
Remote Work :
No
Employment Type :
Full-time
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