ML Ops Lead

📋 Description

• Lead and manage a DevOps team, mentoring engineers while collaborating with Data Science and
• Define infrastructure roadmap for AI/ML workloads and automate provisioning across cloud
• Architect, maintain, and optimise MLOps/LLMOps pipelines and CI/CD frameworks for continuous model
• Deploy Large Language Models (LLMs) into production with high availability and low latency.
• Establish FinOps frameworks to track AI infrastructure spend and manage GPU/CPU cloud budgets.
• Implement auto-scaling, cost controls, and unit economics to optimize resources for training and

🎯 Requirements

• Extensive production experience deploying and supporting ML systems.
• Proven track record leading engineering teams.
• Experience with Generative AI and LLM deployment patterns.
• History of reducing cloud spend on large-scale AI clusters.
• Experience with MLflow, Kubeflow, LangSmith, or Phoenix.
• Expertise in AWS/GCP/Azure cost tools, Kubecost, or Cloudability.

🎁 Benefits

• DEIB-focused and inclusive culture that values diversity, equity, inclusion and belonging.