GenAI MLOps Lead — Scale, Deploy & Optimize AI

Anaplan Inc; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Job summary

Anaplan Inc is seeking a ML Ops Technical Lead to head the infrastructure, cost-optimisation, and deployment strategy for our AI platforms in London. You will lead a skilled DevOps/ML team while staying hands-on on core technical challenges.

You will design scalable MLOps/LLMOps pipelines, deploy GenAI models, and drive FinOps to optimize cloud spend, with emphasis on security, governance, and cost visibility across environments.

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.
• A proven history of reducing cloud spend on large-scale AI clusters.

Responsibilities

• Lead and mentor a DevOps/ML team, collaborating with Data Science and Engineering leaders.
• Define infrastructure roadmap for AI/ML workloads and automate provisioning with IaC.
• Architect and optimise MLOps/LLMOps pipelines and CI/CD for model deployment.
• Deploy LLMs into production with high availability and low latency.
• Establish FinOps, track AI infrastructure spend and budgets.
• Implement auto-scaling and cost-aware policies for efficient training/serving.
• Establish 24/7 observability and incident response for AI platforms.
• Enforce data governance and security across AI/ML infra.

Skills

ML Ops

Team Leadership

GenAI deployment

Cost optimisation

Infrastructure as Code

MLOps pipelines

LLMOps

Kubernetes

Docker

Terraform

Ansible

Jenkins

GitHub Actions

Python

Bash

Go

Tools

MLflow

Kubeflow

LangSmith

Phoenix

Kubecost

Cloudability

Terraform

Job description

Anaplan Inc is seeking a ML Ops Technical Lead to head the infrastructure, cost-optimisation, and deployment strategy for our AI platforms in London. You will lead a skilled DevOps/ML team while staying hands-on on core technical challenges.

You will design scalable MLOps/LLMOps pipelines, deploy GenAI models, and drive FinOps to optimize cloud spend, with emphasis on security, governance, and cost visibility across environments.