Azure ML Engineer

Responsibilities

• Design and implement end-to-end machine learning pipelines on Azure and Databricks.
• Operationalise ML models, including deployment, monitoring, retraining and version management.
• Build and maintain MLOps processes using CI/CD, infrastructure as code and automated testing.
• Define suitable Azure resources, Databricks compute and deployment patterns with a strong focus on cost optimisation.
• Integrate ML solutions with lakehouse-based data platforms and existing data pipelines.
• Collaborate with data engineers, architects, business stakeholders and external vendors.
• Review vendor solutions and ensure alignment with internal architecture, security and operational standards.
• Contribute to reusable ML platform components, technical standards and documentation.

Requirements

• Strong practical experience in ML engineering and MLOps, including deploying, operating, monitoring, retraining and versioning ML models in production.
• Strong Python skills, with practical experience in PySpark or Apache Spark.
• Experience with Azure Databricks, MLflow and preferably Azure Machine Learning.
• Good understanding of lakehouse architecture, Delta Lake, layered data platforms, data modelling, data transformation and designing data structures for analytical and ML use cases.
• Basic knowledge of data quality, metadata, lineage, access-control concepts, governance and integration of ML workloads with enterprise data platforms.
• Knowledge of CI/CD, Git, Azure DevOps and infrastructure-as-code tools such as Terraform or Bicep.
• Experience with cloud resource management, monitoring and cost optimisation.
• Good understanding of end-to-end Azure Data/AI solution architecture, including security, identity, networking and scalable deployment patterns.
• Ability to evaluate architecture alternatives considering performance, security, maintainability and cloud cost.
• Experience collaborating with and technically overseeing external vendors, including architecture and solution reviews.
• Strong communication and interpersonal skills, including listening to clients, working in cross-functional teams, and clear verbal and written communication.
• Fluent English and Hungarian.
• Great numerical and analytical skills.
• Leadership and coaching mindset, team spirit, and ability to foster inclusive, supportive team dynamics.
• Readiness to work in the niche field of higher education, where service and experience are paramount.
• Service-oriented and autonomous approach: responsiveness to client requests, flexibility, versatility, daily proactivity and thirst for learning.
• Commitment to standards and consistency: insists on consistent engineering practices and documentation without creating unnecessary bureaucracy.

Nice to have

• Fluent French would be a plus
• Familiarity with higher education systems and processes is a plus.

What they offer

• Budapest office location (9th district)
• Hybrid work model with 3 days per week home office
• Cafeteria benefit (SZÉP card)
• Training and professional development support
• Private health insurance
• Year-end bonus based on individual and company performance