Data Engineer-Data Scientist – MLOps & Machine Learning

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Data Engineer / Data Scientist – MLOps & Machine Learning

Overview

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.

Must Haves

• 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
• Willingness to work onsite

Responsibilities

• 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

Interested candidates may submit their resumes online or call at 310-906-4780 for further information regarding the position.

NS-DSTB-NS_1779141318

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