Data Engineer
1 week ago
We are seeking a highly skilled Data Engineer - Machine Learning Specialist to join our team at Randstad Digital. As a key member of our data engineering team, you will play a critical role in designing, building, and maintaining scalable and reliable data pipelines and platforms that support the development and deployment of machine learning models and applications.
Key Responsibilities- Collaborate with Cross-Functional Teams
- Work closely with data scientists, software engineers, product managers, and stakeholders to understand the requirements, challenges, and opportunities of machine learning projects.
- Design and Develop Data Pipelines
- Design, build, and maintain scalable and reliable data pipelines and platforms that support the development and deployment of machine learning models and applications.
- Ensure Data Quality and Security
- Apply best practices and tools to ensure the quality, reliability, scalability, and security of machine learning pipelines and systems.
- Optimize Data Pipelines
- Optimize the performance, efficiency, and reliability of the data pipelines and platforms.
- Monitor and Troubleshoot
- Monitor and troubleshoot the data pipelines and platforms and resolve any issues or errors.
- Document and Maintain Standards
- Document and maintain development standards, best practices, and procedures.
- Continuously Learn and Improve
- Continuously learn and improve your skills and knowledge in the fast-changing field of machine learning.
- Education
- Bachelor's degree in computer science, engineering, mathematics, statistics, or related field.
- Experience
- At least 3 years of experience in machine learning, data engineering, or software engineering.
- Technical Skills
- 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 and Skills
- 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.
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