Back
Remote mid-level MLOps role with broad ML+infra requirements and recognizable consumer AI brand increases applicant competition.
Role requires specialized MLOps and ML engineering skills, limiting cross-industry transferability.
Requires 3+ years MLOps experience and mandatory Kubernetes, cloud, CI/CD, and ML framework skills.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own and maintain the on-premises MLOps infrastructure ensuring reliable uptime and performance of production ML models.
Collaborate with data scientists, researchers, and software engineers to transition ML research artifacts into robust, monitored, and continuously improving production services.
Develop, optimize, and monitor ML deployment pipelines, including CI/CD workflows, testing, and system troubleshooting across the ML stack.
3+ years experience in MLOps or full stack Machine Learning.
Proficiency in modern programming languages relevant to ML (e.g., Python, Scientific Python Stack, CUDA).
Experience with Kubernetes and cloud services (GCP/AWS/Azure), CI/CD pipelines, and common ML frameworks/data management.
Work Experience Required: 3+ years in MLOps or related ML engineering roles.
Experienced in bridging ML research and production environments with operational MLOps expertise.
Comfortable working hands-on across Linux, Docker, Kubernetes, and the full ML infrastructure stack to ensure scalability and performance.
Familiar with designing high-standard engineering practices including automated testing, code reviews, and continuous integration for ML systems.