Sr Data Engineer - Machine Learning

Randstad - Charlotte, NC

Full-time

Charlotte, NC

Administrative and Support Services

About the position

The Machine Learning Data Engineer will collaborate with data scientists, software engineers, product managers, and stakeholders to design, build, and maintain scalable data pipelines and platforms that support machine learning projects. This role focuses on ensuring the quality, reliability, and security of machine learning systems while continuously optimizing performance and efficiency.

Responsibilities

• Work closely with data scientists, software engineers, product managers, and stakeholders to understand the requirements, challenges, and opportunities of machine learning projects.
• Design, build, and maintain scalable and reliable data pipelines and platforms that support the development and deployment of machine learning models and applications.
• Apply best practices and tools to ensure the quality, reliability, scalability, and security of machine learning pipelines and systems.
• Optimize the performance, efficiency, and reliability of the data pipelines and platforms.
• Monitor and troubleshoot the data pipelines and platforms and resolve any issues or errors.
• Document and maintain development standards, best practices, and procedures.
• Continuously learn and improve your skills and knowledge in the fast-changing field of machine learning.

Requirements

• Bachelor's degree in computer science, engineering, mathematics, statistics, or related field.
• At least 3 years of experience in machine learning, data engineering, or software engineering.
• Proficient in Python and one or more of the following frameworks: TensorFlow, PyTorch, Scikit-learn, Keras, etc.
• Familiar with programming languages such as Java, Scala.
• Experience with data processing and storage technologies such as Spark, Hadoop, SQL, NoSQL, etc.
• Experience with cloud computing platforms and services such as AWS, Azure, GCP, etc.
• Experience with AWS SageMaker, AWS Glue, AWS EMR is a plus.
• Experience with machine learning lifecycle management tools such as MLflow, Kubeflow, Airflow, etc.
• Experience with containerization and orchestration technologies such as Docker, Kubernetes, etc.
• Experience with version control, testing, and CI/CD tools such as Git, GitHub, Jenkins, etc.
• Knowledge of machine learning concepts and techniques such as supervised, unsupervised, and reinforcement learning, deep learning, natural language processing, computer vision, etc.
• Ability to communicate effectively and collaborate with cross-functional teams.
• Ability to work independently and creatively, and solve complex problems.
• Passion for learning and innovation.