We are:
Wizeline a global AI-native technology solutions provider develops cutting-edgeAI-powereddigital products and platforms. We partner with clients to leverage data and AI accelerating market entry and driving business transformation. As a global community of innovators we foster a culture ofgrowth collaborationandimpact.
With the right people and the right ideas theres no limit to what we can achieve
Are you a fit
Sounds awesome right Now lets make sure youre a good fit for the role:
Key Responsibilities
- Architect end-to-end ML infrastructure including pipelines model serving monitoring and governance.
- Lead deployment of high-impact ML solutions such as forecasting engines optimization models and NLP use cases.
- Design and manage advanced CI/CD workflows using Azure Pipelines MLflow and Databricks.
- Implement model registry versioning lineage and audit-compliant governance frameworks.
- Build and maintain monitoring systems to detect model drift and automate retraining cycles.
- Mentor MLOps engineers and collaborate with platform data and product teams to ensure seamless integration.
- Drive adoption of MLOps best practices across containerization observability testing and scalable infrastructure.
Must-have Skills
- 58 years of experience in ML Engineering MLOps or building large-scale ML systems.
- Strong expertise with Spark Azure Databricks MLflow Kubernetes and Docker.
- Proven track record deploying ML solutions at enterprise scale with audit governance and monitoring layers.
- Experience designing ML infrastructure and CI/CD pipelines in cloud environments.
- Knowledge of hybrid or multi-cloud architectures.
- Bachelors degree required; Masters preferred in Computer Science Engineering or related fields.
Nice-to-have:
- AI Tooling Proficiency: Leverage one or more AI tools to optimize and augment day-to-day work including drafting analysis research or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows.
What we offer:
- A High-Impact Environment
- Commitment to Professional Development
- Flexible and Collaborative Culture
- Global Opportunities
- Vibrant Community
- Total Rewards
*Specific benefits are determined by the employment type and location.
Find out more about our culturehere.
We are:Wizeline a global AI-native technology solutions provider develops cutting-edgeAI-powereddigital products and platforms. We partner with clients to leverage data and AI accelerating market entry and driving business transformation. As a global community of innovators we foster a culture ofgro...
We are:
Wizeline a global AI-native technology solutions provider develops cutting-edgeAI-powereddigital products and platforms. We partner with clients to leverage data and AI accelerating market entry and driving business transformation. As a global community of innovators we foster a culture ofgrowth collaborationandimpact.
With the right people and the right ideas theres no limit to what we can achieve
Are you a fit
Sounds awesome right Now lets make sure youre a good fit for the role:
Key Responsibilities
- Architect end-to-end ML infrastructure including pipelines model serving monitoring and governance.
- Lead deployment of high-impact ML solutions such as forecasting engines optimization models and NLP use cases.
- Design and manage advanced CI/CD workflows using Azure Pipelines MLflow and Databricks.
- Implement model registry versioning lineage and audit-compliant governance frameworks.
- Build and maintain monitoring systems to detect model drift and automate retraining cycles.
- Mentor MLOps engineers and collaborate with platform data and product teams to ensure seamless integration.
- Drive adoption of MLOps best practices across containerization observability testing and scalable infrastructure.
Must-have Skills
- 58 years of experience in ML Engineering MLOps or building large-scale ML systems.
- Strong expertise with Spark Azure Databricks MLflow Kubernetes and Docker.
- Proven track record deploying ML solutions at enterprise scale with audit governance and monitoring layers.
- Experience designing ML infrastructure and CI/CD pipelines in cloud environments.
- Knowledge of hybrid or multi-cloud architectures.
- Bachelors degree required; Masters preferred in Computer Science Engineering or related fields.
Nice-to-have:
- AI Tooling Proficiency: Leverage one or more AI tools to optimize and augment day-to-day work including drafting analysis research or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows.
What we offer:
- A High-Impact Environment
- Commitment to Professional Development
- Flexible and Collaborative Culture
- Global Opportunities
- Vibrant Community
- Total Rewards
*Specific benefits are determined by the employment type and location.
Find out more about our culturehere.
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