The Data Scientist MLOps will be responsible for building scalable data pipelines, deploying ML models into production, and ensuring performance, scalability, and system availability. The role requires strong expertise in data engineering, orchestration frameworks, cloud infrastructure, and containerized environments.
Key Responsibilities
- Design and implement scalable data pipelines and RESTful APIs.
- Develop and maintain production-ready ML deployment systems.
- Work with Big Data technologies including Spark, Hive, Snowflake, and Databricks.
- Implement orchestration and messaging frameworks such as Jenkins and Kafka.
- Deploy and manage ML workloads on Azure cloud environments.
- Handle container orchestration using Kubernetes and Docker Swarm.
- Ensure system scalability, reliability, and performance optimization.
- Collaborate using Git, Jira, and Confluence for agile delivery.
Required Skills Qualifications
- Bachelors or Masters degree in Computer Science, Engineering, or related field.
- Strong Python expertise including Pandas, NumPy, Flask, SQLAlchemy.
- Experience with Azure cloud and MLOps frameworks.
- Proficiency in Linux, containerization, CI/CD pipelines, and distributed systems.
- Strong understanding of data structures, algorithms, and scalable architecture.
This job is provided by Shine.com
