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Mid-level MLOps in a metro with broad cloud and ML skill requirements increases applicant competition.
MLOps skills transfer across industries but require ML domain experience, yielding moderate transferability.
Explicit 3–5 years plus mandatory ML, cloud, Docker/Kubernetes and CI/CD skills raises screening strictness.
Design and maintain scalable machine learning pipelines covering training, validation, deployment, and monitoring in production environments.
Automate CI/CD processes for ML models and optimize ML infrastructure for scalability, reliability, and cost-efficiency.
Collaborate with data scientists to operationalize models, implement version control, troubleshoot deployment issues, and maintain documentation for ML workflows.
3-5 years of experience in software development, DevOps, or data engineering.
Proficiency with 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, GCP), CI/CD pipelines, and version control (Git).
Bachelor's degree in Computer Science Engineering, IT Engineering, or related engineering field.
Has hands-on experience designing and deploying ML systems in production with strong operational ownership over ML pipelines and infrastructure.
Skilled in cloud-based ML services and container orchestration with a focus on automation and monitoring.
Comfortable troubleshooting model deployment issues and collaborating cross-functionally with data science teams to improve production ML workflows.