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High due to strong brand, metro locations, mid-level MLOps role with broad skill requirements.
Medium because core DevOps skills transfer across industries but MLOps and agent-framework experience favors AI-centric backgrounds.
High because explicit 4–8 years requirement plus mandatory MLOps/DevOps tools and cloud/Kubernetes expertise.
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Build and maintain CI/CD pipelines for deployment of agents and ML models ensuring reliable scaling and monitoring of multi-agent media tools.
Manage containerized applications using Docker and Kubernetes across cloud or on-premise environments, focusing on infrastructure automation and performance optimization.
Ensure compliance with security and data governance policies while collaborating with engineers and data scientists to streamline model deployment and retraining.
4 to 8 years of experience in DevOps/MLOps or related roles.
Strong hands-on experience with cloud platforms (AWS, Azure, GCP) and Kubernetes.
Proficiency in CI/CD tools such as GitHub Actions, Jenkins, ArgoCD.
Experience with agentic frameworks (e.g., LangChain, AutoGen, CrewAI) and ML lifecycle management tools (MLflow, Kubeflow, Weights & Biases).
Experienced in deploying and managing multi-agent AI systems or agentic frameworks, indicating strategic fit for advanced AI orchestration.
Skilled in optimizing system reliability, scalability, and cost-efficiency with a proactive approach to identifying bottlenecks.
Comfortable working collaboratively across engineering and data science teams in a dynamic, tech-driven environment.