machine learning engineer

Overview

Who you'll be working for

What requirements you'll need to be eligible

• 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.

What you'll be doing on the job

• 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.

Consultant Contact