Platform Lead – ML Ops

Platform Lead – ML Ops Overview

At Anaplan, we are a team of innovative professionals dedicated to enhancing business decision-making through our advanced AI-infused scenario planning and analysis platform. Our platform is trusted by over 2,400 leading global companies, including Fortune 50 giants like Coca-Cola, LinkedIn, Adobe, LVMH, and Bayer. We foster a Winning Culture characterized by diversity, leadership, ambition, and celebration of success, all supported by strategic, value-driven, and disciplined operational principles.

Role Overview

We are looking for a talented Platform Lead to take charge of our ML Ops infrastructure, focusing on scalability, cost management, and deployment strategies. You will lead a skilled team of DevOps engineers, maintaining a hands-on technical approach. Your main responsibilities include building, scaling, and securing the foundational platforms for machine learning and generative AI models, all while managing budgets effectively.

Key Responsibilities

• Guide and manage a dedicated DevOps team, mentoring engineers at various levels and working closely with Data Science and Engineering leadership.
• Develop and execute infrastructure strategies for AI/ML workloads, automating resource provisioning with Infrastructure as Code (IaC).
• Design, develop, and optimize CI/CD pipelines for continuous deployment of ML models and large language models (LLMs).
• Deploy large language models into production environments, ensuring they operate with high availability, low latency, and peak performance for Generative AI applications.
• Implement FinOps practices to monitor, allocate, and forecast cloud infrastructure costs, especially for GPU and CPU resources.
• Apply auto-scaling, spot instances, and resource down-scaling policies to maximize efficiency and reduce waste, providing full visibility into the economics of training and serving models.
• Establish 24/7 incident response and telemetry systems to monitor system health, performance, and data pipeline integrity.
• Ensure data governance, security, and compliance protocols are strictly enforced across all AI/ML infrastructure components.

Required Skills and Experience

• Significant hands-on experience deploying and supporting machine learning systems in production environments.
• Proven leadership record in managing engineering teams effectively.
• Experience with deploying Generative AI models and large language models, understanding deployment patterns and challenges.
• Track record of reducing cloud spend on large-scale AI clusters through optimization techniques.
• Familiarity with ML workflow management tools such as MLflow, Kubeflow, LangSmith, or Phoenix.
• Expertise with cloud cost management tools like AWS Cost Explorer, GCP Cost Tools, Azure Cost Management, Kubecost, or Cloudability.
• Deep knowledge of Kubernetes, Docker, and service mesh architectures.
• Proficiency with automation and CI/CD tools such as Terraform, Ansible, Jenkins, or GitHub Actions.
• Strong programming skills in Python, Bash, or Go.
• Knowledge of inference serving platforms including Triton Inference Server, vLLM, or Hugging Face TGI.

Our Commitment to Diversity and Inclusion

We believe that fostering an inclusive environment that values diversity of thought and experience enhances our innovation and success. We are committed to providing equal opportunities and reasonable accommodations for all candidates, regardless of gender, ethnicity, age, disability, or other protected characteristics. We encourage everyone to bring their authentic selves to work and support a culture of respect and belonging.

Note: The company has issued a warning about fraudulent job offers circulating online. Genuine offers are extended only after thorough interviews, and all official communication will come from @anaplan.com email addresses. Candidates are advised to verify communications before responding.

Degree Requirement: Not Specified

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