Key responsibilities for this role include designing, developing and maintaining robust data infrastructure across real-time and batch workloads.
The successful candidate will be responsible for building and supporting machine learning pipelines for model training, deployment and monitoring.
Collaboration with cross-functional teams including data scientists, engineers and product teams is also required to deliver high-performance data and machine learning solutions.
* Develop scalable, fault-tolerant data infrastructure using cloud-based services such as GCP, AWS or Azure.
* Design and implement efficient machine learning pipelines that integrate with existing data platforms.
* Work closely with data scientists to understand business requirements and develop data-driven solutions.
Requirements:
5+ years of experience in a related field (Data Engineer or MLOps Engineer).
Strong expertise in programming languages such as Python, Java or Scala.
Hands-on experience with container orchestration tools like Docker and Kubernetes.
Experience with big data technologies including Apache Kafka, Spark and Hadoop.
Benefits:
This is a top-tier hourly rate paid in USD, long-term contract opportunity with fully remote work and a high-impact role within a data-driven organization.
Opportunity to work on cutting-edge technology and contribute to the development of innovative data solutions.
Chance to collaborate with experienced professionals in the field and learn from their expertise.
Tech Stack & Requirements:
Familiarity with Apache Kafka, Spark or similar tools is highly desirable.
Experience with ETL, CI/CD, git and monitoring pipelines is also an asset.
Strong communication skills and fluency in English are mandatory.
Bachelor's or Master's degree in Computer Science or related fields.