Design, develop, and maintain Databricks notebooks and jobs supporting data transformation, analytics, and machine learning workloads.
Develop, integrate, and deploy custom machine learning models using tools such as Azure Machine Learning and MLflow.
Implement data and AI use cases on the Databricks platform, ensuring seamless integration and operational reliability.
Configure and manage external AI models within Databricks Model Serving, including secure management of API keys.
Build scalable asynchronous agents using native Python async patterns to support higher‑concurrency workloads.
Apply CI/CD and DevOps best practices using Azure DevOps to automate deployment and management of data pipelines and ML infrastructure.
Inspect and interpret data, metadata, and database structures using SQL editors and built‑in visualizations.
Collaborate closely with data scientists, analysts, and engineers to translate requirements into production‑ready ML solutions.
Write clean, modular, well‑documented, and testable Python code following established engineering standards.

5 years of experience designing, developing, and deploying machine learning solutions on the Databricks platform.
Proficiency in Python, including writing modular, testable code and leveraging async programming patterns.
Familiarity with machine learning concepts, tools, and libraries such as TensorFlow, PyTorch, Scikit‑learn, and MLflow.
Experience configuring and integrating external AI models and working with AI governance and monitoring capabilities.
Understanding of CI/CD and DevOps practices supporting data and ML workloads.
Ability to analyze data and metadata using SQL and visualization tools.
Strong problem‑solving, collaboration, and communication skills.

Senior ML, MLOps Engineer

Key skills