Job description

We are partnering with one of our staffing partners who is searching for a Data engineer with Machine learning experience to design, develop, and optimize scalable data pipelines and infrastructure for ML-driven applications. The ideal candidate will have experience in big data technologies, ETL processes, and model deployment to support advanced analytics and AI initiatives.Salary $50k-70k slight flexibility for a good candidate.Due to low rates only direct applicants no c2c candidates please. Candidates will be reached directly by our staffing partner and client Responsibilities • Design and build scalable data pipelines to support machine learning workflows. • Develop, optimize, and maintain ETL processes for structured and unstructured data. • Work with big data frameworks (Apache Spark, Hadoop, Databricks) to process large datasets. • Collaborate with data scientists to integrate ML models into production environments. • Implement and manage ML model deployment and monitoring • Optimize data storage, retrieval, and performance for machine learning applications. • Ensure data integrity, security, and compliance with industry standards. • Automate data processing workflows using Python, SQL, or Scala. Requirements • Strong experience in data engineering and machine learning workflows. • Hands-on experience with big data technologies (Apache Spark, Hadoop, Snowflake). • Proficiency in SQL and Python, with experience in ML libraries (TensorFlow, PyTorch, Scikit-learn). • Knowledge of cloud platforms (AWS, Azure, Google Cloud Platform) for ML model deployment. • Familiarity with DevOps and MLOps tools, such as: • CI/CD Pipelines for ML (MLflow, Kubeflow) • Containerization (Docker, Kubernetes) • Infrastructure as Code (Terraform, Ansible) • Version Control (Git, Bitbucket) • Strong understanding of data governance, security, and best practices.

Requirements