Senior Azure & MLOPs Engineer

Job purpose

As an MLOps Engineer, you will be responsible for building and maintaining scalable, secure, and automated AI systems on the Azure cloud platform. You will enable seamless deployment, monitoring, and lifecycle management of AI models and services using Azure AI capabilities, ensuring reliability and compliance across environments. Your contributions will have a direct impact on our ability to deliver innovative solutions and drive business growth.

You will combine your machine learning, software, and data engineering skills to design, develop, and deploy AI systems that solve business problems and provide value directly to our business unit and customers. This role involves collaborating with data scientists and engineering teams to implement CI/CD pipelines for AI, optimize workflows, and drive operational excellence for enterprise-grade AI solutions.

Responsibilities and activities

Core Activities

- Design, implement, and maintain MLOps pipelines for AI/ML assets deployed as managed online endpoints in Azure Machine Learning.
- Automate deployment, monitoring, and lifecycle management of AI systems, including Azure Speech Services and OpenAI models.
- Collaborate with data scientists and engineering teams to integrate AI/ML assets into production environments.
- Implement CI/CD workflows for AI solutions using Azure Dev Ops and Azure CLI.
- Ensure compliance, security, and scalability of AI systems across environments.
- Build monitoring dashboards to track performance, data drift, and system health, and implement alerting and remediation strategies.
- Manage promotion of AI/ML assets (models, apps, containers) across development, staging, and production environments.
- Optimize resource utilization and cost efficiency for AI workloads in Azure.

Additionally:

- Contribute to best practices and standards for MLOps within the organization.
- Support troubleshooting and incident resolution for AI systems in production.
- Explore and evaluate new Azure AI services and tools to enhance operational capabilities.
- Document processes, workflows, and operational guidelines for knowledge sharing.
- Assist in deploying and maintaining Flask-based applications that consume Azure ML endpoints.

Qualifications, skills and experience

Mandatory Skills

- 3+ years of experience in MLOps or related roles with a focus on Azure cloud platform.
- Strong proficiency in Azure AI services (Azure Machine Learning, Cognitive Services, Azure Speech Services, Azure OpenAI).
- Hands-on experience with CI/CD pipelines using Azure Dev Ops and Azure CLI.
- Proficiency in Python and experience with Azure SDKs for AI services.
- Solid understanding of containerization (Docker) and orchestration (Kubernetes, AKS).

- Experience with monitoring and logging solutions for AI systems (Azure Monitor, Application Insights) and building dashboards.
- Knowledge of identity and access management in Azure (Managed Identity, RBAC).
- Strong understanding of AI/ML asset lifecycle management, including promotion across environments.
- Strong problem-solving skills and the ability to work in a collaborative team environment.
- Excellent communication and documentation skills. Optional Skills

- Experience with Git Hub Actions or other CI/CD tools.
- Familiarity with Flask or similar frameworks for serving AI endpoints.
- Exposure to MLflow or similar model tracking tools.
- Knowledge of generative AI and large language models (LLMs) in production environments.
- Understanding of compliance frameworks (GDPR) for AI solutions.
- Experience working in an Agile or Scrum development environment is a plus.