ML Ops Lead: GenAI Deployment & FinOps Leader

Anaplan; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Anaplan is seeking a ML Ops Technical Lead to spearhead infrastructure, cost-optimisation, and deployment strategies. You will manage a DevOps team while remaining hands-on, building secure platforms for ML and GenAI models with financial accountability.

Lead MLOps/LLMOps pipelines, define IaC strategies, and deploy GenAI workloads with high availability while optimising cloud spend and ensuring governance across AI infrastructure.

Qualifications

• Extensive production experience deploying and supporting ML systems.
• Proven track record of leading engineering teams.
• Demonstrated experience with Generative AI and LLM deployment patterns.
• History of reducing cloud spend on large-scale AI clusters.

Responsibilities

• Team Leadership & Collaboration: lead and mentor a DevOps team with cross-functional partnerships.
• Infrastructure Strategy & Automation: define AI/ML infrastructure roadmap and IaC provisioning.
• MLOps & LLMOps Engineering: architect and optimise pipelines and CI/CD for models.
• GenAI Production Deployment: deploy LLMs with high availability and performance.
• FinOps & Budget Management: track and forecast AI infrastructure spend.
• Resource Efficiency: implement autoscaling and cost-controls for training/serving.
• Observability & Incident Response: 24/7 monitoring, alerting, and data pipelines visibility.
• Data Governance & Security: enforce governance and security across platforms.

Skills

DevOps leadership

MLOps pipelines

LLMOps

Kubernetes

Docker

Terraform

Python

Go

Cloud cost optimization

CI/CD

Monitoring

Data governance

Tools

MLflow

Kubeflow

LangSmith

Phoenix

Kubecost

Cloudability

Terraform

Ansible

Jenkins

GitHub Actions

Job description

Anaplan is seeking a ML Ops Technical Lead to spearhead infrastructure, cost-optimisation, and deployment strategies. You will manage a DevOps team while remaining hands-on, building secure platforms for ML and GenAI models with financial accountability.

Lead MLOps/LLMOps pipelines, define IaC strategies, and deploy GenAI workloads with high availability while optimising cloud spend and ensuring governance across AI infrastructure.