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We are looking for an experienced Lead MLOps Engineer to make our team even stronger.
The ideal candidate will combine deep knowledge of machine learning operations with a pragmatic approach to operational excellence, delivering robust, scalable, and efficient solutions for deploying and managing ML pipelines and models across our diverse project stack.
Responsibilities
- Design and manage MLOps pipelines ensuring standardization, scalability, and real-time monitoring
- Implement continuous integration and continuous deployment (CI/CD) pipelines alongside incident response procedures
- Optimize model performance and manage lifecycle from development to deployment
- Lead the strategy for model scalability across Python and other predictive analytics environments
- Coordinate with data science teams to ensure that models are efficiently integrated and maintained in production systems
Requirements
- Minimum 3 years of experience in MLOps or ML engineering roles
- Expertise in Databricks and Azure cloud platforms
- Proficiency in MLOps tooling and monitoring tools
- Background in implementing CI/CD frameworks such as Jenkins, Concourse, or GitLab CI/CD
- Excellent communication skills and capacity to collaborate effectively with diverse teams
- Understanding of Python, LLM, and large data processing frameworks like Spark
- Qualifications in GCP Cube flow pipelines, Vertex AI, or familiarity with these technologies
Technologies
- Python, LLM
- Predictions, Large data, Spark
- GCP Cube flow pipelines (optional)
- Vertex AI
- Databricks