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Mid-level MLOps role with niche skillset and moderate employer brand.
MLOps skills are broadly transferable across industries but require ML domain experience.
Explicit 3–5 year requirement plus mandatory ML, cloud, Docker, Kubernetes, and CI/CD skills.
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Design, implement, and maintain scalable ML pipelines for training, validation, and deployment in production environments.
Automate CI/CD processes for ML models including monitoring model performance, data drift, and system health.
Collaborate with data scientists to operationalize models, optimize infrastructure for scalability and cost, and troubleshoot production issues.
3-5 years of experience in software development, DevOps, or data engineering.
Proficiency in Python, SQL, and at least one ML framework such as TensorFlow, PyTorch, or Scikit-learn.
Experience with containerization (Docker), orchestration (Kubernetes), cloud platforms (AWS, Azure, or GCP), and CI/CD pipelines.
Bachelor's degree in Computer Science, IT, or related engineering field.
Hands-on expertise operating ML production systems with strong experience in MLOps tools and cloud ML services.
Experience managing end-to-end ML lifecycle including version control, monitoring, and automation in collaborative environments.
Ability to optimize ML infrastructure for reliability and cost-effectiveness and handle on-call rotational support.