| ML Ops Engineer to drive the full lifecycle of machine learning solutions-from data exploration and model development to scalable deployment and monitoring. This role bridges the gap between data science model development and production-grade ML Ops Engineering.
Key Responsibilities - 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
Qualifications - 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.
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ML Ops Engineer to drive the full lifecycle of machine learning solutions-from data exploration and model development to scalable deployment and monitoring. This role bridges the gap between data science model development and production-grade ML Ops Engineering. Key Responsibilities Develop pr...
| ML Ops Engineer to drive the full lifecycle of machine learning solutions-from data exploration and model development to scalable deployment and monitoring. This role bridges the gap between data science model development and production-grade ML Ops Engineering.
Key Responsibilities - 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
Qualifications - 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.
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Job Location:
Monthly Salary:
Not Disclosed
Posted on:
09-09-2025
Vacancies:
1 Vacancy
