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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

Lead MLOps Engineer

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