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Strong employer brand and mid-level role but niche MLOps skills moderate competition.
Requires specialized ML infrastructure and cloud skills, limiting transferability across industries.
Explicit 5+ years and mandatory Python, Terraform, Kubernetes, GCP and MLOps experience.
Lead design, delivery, and operation of production-grade MLOps/AIOps systems to transition ML/AI solutions into scalable, reliable production.
Own and evolve CI/CD pipelines, cloud infrastructure (GCP), deployment, monitoring, lifecycle management, and standards for Data Science and AI teams.
Ensure reliability, security, compliance, and mentor engineers to elevate engineering practices and continuous improvement.
Bachelor’s degree in Computer Science, Engineering, or related field; Master’s preferred.
Approximately 5+ years of experience in MLOps, ML engineering, or cloud engineering.
Strong hands-on skills in Python, Terraform, Docker, Kubernetes, and deep familiarity with Google Cloud Platform (GCP) ecosystem.
Not explicitly mentioned in the JD: Notice period.
Experienced in leading production deployment and operation of scalable ML, AI, and agent systems with reliability and security focus.
Skilled in architecting and optimizing cloud infrastructure using Infrastructure as Code (Terraform) within GCP.
Capable of cross-team enablement and establishing enterprise-grade MLOps engineering patterns and standards.