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Seeking a highly analytical and hands-on Data Engineer / Data Scientist with strong MLOps experience in Databricks environments. This role requires a strong foundation in machine learning theory, model development, and large-scale cloud-based data platforms. The ideal candidate should be comfortable working across engineering and data science teams while supporting end-to-end ML workflows in production environments.
Job Responsibility:
Design, build, and support scalable machine learning and data workflows in cloud environments
Develop and maintain MLOps pipelines using Databricks and MLflow
Track, manage, and deploy machine learning models across environments
Work closely with data scientists, engineers, and business stakeholders to deliver ML-driven solutions
Analyze large datasets and apply machine learning techniques to solve business problems
Support model monitoring, optimization, and continuous improvement initiatives
Collaborate on architecture, best practices, and scalable ML engineering standards
Requirements:
5+ years of hands-on experience as a Data Engineer or Data Scientist in large-scale, cloud-based data environments
Strong experience building or supporting MLOps workflows in Databricks
Hands-on experience with MLflow, including experiment tracking, model registry, and deployment workflows
Strong understanding of machine learning fundamentals and model lifecycle management
Solid data science foundation with understanding of ML theory, mathematics, and statistical concepts
Experience building, training, validating, and deploying machine learning models
Ability to explain model selection decisions and ML approaches clearly
Strong collaboration and communication skills with the ability to work well across cross-functional teams
Strong personality fit and ability to work effectively in team-oriented environments