(Remote)

​You will be designing, building and maintaining data pipelines and machine learning infrastructure that support scalable, reliable, and production-ready AI/ML solutions. You will work closely with data scientists, engineers, and product teams to operationalize models, streamline workflows, and ensure data quality and availability.

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• Develop and maintain data pipelines to support machine learning and analytics use cases.

• Implement MLOps best practices for model deployment, monitoring, and lifecycle management.

• Build and optimize ETL/ELT processes for structured and unstructured data.

• Automate workflows for training, testing, and deploying ML models.

• Ensure data integrity, governance, and security across the ML lifecycle.
  

MLOps Data Engineer Experience

• Strong programming skills in Python, SQL, and experience with AWS

• Proficiency with data engineering tools (e.g., Spark, Kafka, Airflow, dbt).

• Hands-on experience with MLOps frameworks (e.g., MLflow, Kubeflow, Vertex AI, SageMaker).

• Familiarity with CI/CD pipelines, containerization (Docker, Kubernetes)

• Solid understanding of data modeling, warehousing, and APIs.

• Strong problem-solving skills and ability to work in agile environments.