A
Anaplan
Platform Lead (ML Ops)
Skills
Google Cloud PlatformDockerPythonGolangData ScienceJenkinsMachine LearningAIAWSAnsibleGitHubKubernetesComplianceTerraformAzure
2 months ago
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Important information
Contract type:
Permanent
Location:
London, UK
Work mode:
Onsite
Published on:
4 September 2026
What they need
- We are seeking a Platform Lead to spearhead our ML Ops infrastructure, cost-optimisation, and deployment strategies. You will manage a talented team of DevOps engineers while remaining deeply technical and hands-on. Your primary mission is to build, scale, and secure the foundational platforms for our machine learning (ML) and generative AI (GenAI) models while maintaining financial accountability
- Team Leadership & Collaboration: Lead and manage a dedicated DevOps team, mentoring both junior and senior engineers while collaborating closely with Data Science and Engineering leaders
- Infrastructure Strategy & Automation: Define the infrastructure roadmap for AI/ML workloads and automate provisioning across cloud environments using Infrastructure as Code (IaC)
- MLOps & LLMOps Engineering: Architect, maintain, and optimise robust MLOps/LLMOps pipelines and CI/CD frameworks for continuous model deployment
- GenAI Production Deployment: Deploy Large Language Models (LLMs) into production environments, ensuring high availability, low latency, and optimal performance for GenAI applications
- FinOps & Budget Management: Establish FinOps frameworks to track, allocate, and forecast AI infrastructure spend, managing high-cost GPU/CPU cloud budgets
- Resource Efficiency & Unit Economics: Implement auto-scaling, spot instances, and down-scaling policies to eliminate waste, while providing full visibility into the unit economics of training and serving LLM models
- Observability & Incident Response: Establish 24/7 incident response, telemetry, and observability metrics to monitor system performance, model drift, and data pipelines
- Data Governance & Security: Enforce strict data governance, platform security, and compliance protocols across all AI/ML infrastructure- Expert knowledge of Terraform, Ansible, Jenkins, or GitHub Actions
- Experience with tools like MLflow, Kubeflow, LangSmith, or Phoenix
- Demonstrated experience with Generative AI and LLM deployment patterns
- Familiarity with Triton Inference Server, vLLM, or Hugging Face TGI
- Extensive production experience deploying and supporting ML systems
- Extensive background of Kubernetes (K8s), Docker, and service meshes
- A proven history of reducing cloud spend on large-scale AI clusters
- Proficient in Python, Bash, or Go
- Expertise in AWS/GCP/Azure cost tools, Kubecost, or Cloudability
- Proven track record of leading engineering teams
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