Job Summary:

Become a part of our Regional Data & BI Team as a Machine Learning Engineer. In this role, you’ll focus on creating and refining MLOps pipelines, automating campaigns, developing data pipelines, and delivering insights to guide business decisions. This role is ideal for those proficient in machine learning and model operationalization who enjoy working within a dynamic environment.

Key Responsibilities:

• Collaborate with cross-functional teams to transform machine learning prototypes into scalable, production-ready systems.
• Design and implement MLOps architecture and pipelines that align with organizational and industry standards.
• Conduct exploratory data analysis to uncover trends, develop models, and provide actionable insights.
• Build and manage data pipelines, ensuring robust production job monitoring, alerting, and data quality.
• Refactor and optimize ML training and inference code, incorporating feature stores and tools like MLFlow for production-readiness.
• Design and maintain CI/CD pipelines for deploying machine learning models and Databricks assets.
• Implement DataOps and MLOps pipelines to streamline and automate machine learning workflows.
• Create and maintain production dashboards to monitor campaign performance and key business metrics.
• Collaborate with DevOps teams to integrate machine learning models into broader CI/CD pipelines.
• Support ongoing R&D of machine learning use cases by analyzing and mapping data to requirements.
• Develop data visualizations and storytelling tools to communicate trends and insights to stakeholders effectively.
• Stay updated on the latest advancements in machine learning, MLOps, and data engineering to drive innovation.
• Document workflows, processes, and technical details to ensure transparency and alignment across teams.

Qualifications:

• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
• Minimum of 3 years of experience in machine learning operations or data engineering roles.
• Proven experience in developing, deploying, and optimizing machine learning models in production environments.
• Strong understanding of MLOps principles, machine learning workflows, and data engineering best practices.
• Proficiency in Python (preferred), Java, or R, with experience in libraries such as TensorFlow, PyTorch, or Scikit-Learn.
• Expertise in Databricks workflows, including MLFlow and feature store integration.
• Hands-on experience with cloud platforms like Azure (preferred), AWS, or GCP.
• Proficiency in SQL, Apache Spark, and data manipulation tools such as Pandas.
• Experience with data visualization tools like Tableau, Power BI, or similar platforms.
• Familiarity with CI/CD pipelines and tools such as Azure DevOps, or GitLab CI.
• Knowledge of ETL development, data profiling, and data quality management.
• Strong ability to identify data patterns, trends, and insights, and effectively communicate them through data storytelling.
• Experience in optimizing machine learning models for performance and cost efficiency in cloud environments.
• Excellent problem-solving skills and ability to work collaboratively in a team environment.
• Strong communication skills to convey technical concepts effectively to non-technical stakeholders.
• Ability to work independently, adapt to changing priorities, and manage multiple tasks in a fast-paced environment.