ML Ops Engineer

Anaplan Inc; Greater London; On-site; GBP 110,000 - 140,000; Full time

Job summary

Anaplan Inc is seeking a ML Ops Engineer to join our Platform Engineering team. You will design, scale, and maintain high-performance MLOps and LLMOps infrastructure supporting our AI-infused scenario planning platform.

You will collaborate with Data Scientists, ML Engineers, and Cloud Infrastructure teams to streamline model training, deployment, and inference while optimizing GPU utilisation, reliability, and cost-efficiency.

Qualifications

• Hands-on production experience in DevOps, SRE, or Platform Engineering, with some experience dedicated to AI/ML infrastructure.
• Proven track record of deploying, scaling, and operationalising machine learning models and LLMs in cloud-native production environments.
• Demonstrated experience managing compute-intensive GPU infrastructure and HPC environments.
• Advanced proficiency in Kubernetes (K8s), Docker, Helm, Kubeflow, and service meshes (Istio).
• Hands-on experience with Terraform, Ansible, GitHub Actions, ArgoCD, or Jenkins.
• Experience with vLLM, Ray, MLflow, LangChain / LangSmith, DeepSpeed, or Hugging Face TGI.
• Solid background in AWS / GCP / Azure, Kubecost, and GPU cost optimisation techniques.
• Strong skills in Python, Bash, or Go; deep knowledge of Linux kernel tuning and performance monitoring.

Responsibilities

• Provision and manage cloud-native AI/ML infrastructure utilising Kubernetes, Docker, and GPU orchestration frameworks (e.g., NVIDIA GPU Operator, Slurm, or Ray).
• Automate core platform infrastructure using IaC tools like Terraform, Helm, and Ansible.
• Optimise GPU compute workloads, high-speed networking, and storage for efficient model training and low-latency inference.
• Build and maintain robust CI/CD and MLOps pipelines for continuous model training, evaluation, packaging, and production deployment.
• Deploy Large Language Models (LLMs) and generative AI workloads using advanced inference engines (e.g., Triton Inference Server, vLLM, TensorRT-LLM).
• Enable automated model validation, monitoring for model drift, data drift, and latency bottlenecks.
• Monitor and optimise cloud spend across high-cost GPU/CPU clusters across AWS, GCP, or Azure.
• Implement auto-scaling strategies, spot instance policies, and dynamic resource allocation to eliminate infrastructure waste.
• Establish benchmarking and telemetry to track unit economics and throughput for training and serving AI models.
• Implement end-to-end observability using tools like Prometheus, Grafana, OpenTelemetry, and Weights & Biases or MLflow.

Skills

DevOps/SRE/Platform Engineering

ML model deployment

GPU infrastructure

Kubernetes

Docker

Terraform

Ansible

GitHub Actions

ArgoCD/Jenkins

vLLM/Ray/MLflow

LangChain/LangSmith/DeepSpeed

AWS/GCP/Azure

Tools

Kubernetes

Docker

Helm

Kubeflow

Istio

Terraform

Ansible

GitHub Actions

ArgoCD

Jenkins

Job description

At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market.

What unites Anaplanners across teams and geographies is our collective commitment to our customers’ success and to our Winning Culture.

Our customers rank among the who’s who in the Fortune 50. Coca-Cola, LinkedIn, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best-in-class platform.

Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebratingour wins – big and small.

Supported by operating principles of being strategy-led, values -based and disciplined in execution, you’ll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let’s build what’s next - together!

Role Overview

We are seeking a ML Ops Engineer to join our Platform Engineering team at Anaplan. In this role, you will design, scale, and maintain high-performance MLOps and LLMOps infrastructure supporting our cutting-edge AI-infused scenario planning platform.

You will work closely with Data Scientists, ML Engineers, and Cloud Infrastructure teams to streamline model training, deployment, and inference while ensuring optimal GPU utilisation, reliability, and cost-efficiency.

Your Impact

• Provision and manage cloud-native AI/ML infrastructure utilising Kubernetes, Docker, and GPU orchestration frameworks (e.g., NVIDIA GPU Operator, Slurm, or Ray).
• Automate core platform infrastructure using Infrastructure as Code (IaC) tools like Terraform, Helm, and Ansible.
• Optimise GPU compute workloads, high-speed networking, and storage for efficient model training and low-latency inference.
• Build and maintain robust CI/CD and MLOps pipelines for continuous model training, evaluation, packaging, and production deployment.
• Deploy Large Language Models (LLMs) and generative AI workloads using advanced inference engines (e.g., Triton Inference Server, vLLM, TensorRT-LLM).
• Enable automated model validation, monitoring for model drift, data drift, and latency bottlenecks.
• Monitor and optimise cloud spend across high-cost GPU/CPU clusters across AWS, GCP, or Azure.
• Implement auto-scaling strategies, spot instance policies, and dynamic resource allocation to eliminate infrastructure waste.
• Establish benchmarking and telemetry to track unit economics and throughput for training and serving AI models.
• Implement end-to-end observability using tools like Prometheus, Grafana, OpenTelemetry, and Weights & Biases or MLflow.

Your Skills

• Hands-on production experience in DevOps, Site Reliability Engineering (SRE), or Platform Engineering, with some experience dedicated to AI/ML infrastructure.
• Proven track record of deploying, scaling, and operationalising machine learning models and LLMs in cloud-native production environments.
• Demonstrated experience managing compute-intensive GPU infrastructure and high-performance computing (HPC) environments.
• Advanced proficiency in Kubernetes (K8s), Docker, Helm, KubeFlow, and service meshes (e.g., Istio).
• Hands-on experience with Terraform, Ansible, GitHub Actions, ArgoCD, or Jenkins.
• Experience with vLLM, Ray, MLflow, LangChain / LangSmith, DeepSpeed, or Hugging Face TGI.
• Solid background in AWS / GCP / Azure, Kubecost, and GPU cost optimisation techniques.
• Strong skills in Python, Bash, or Go; deep knowledge of Linux kernel tuning and performance monitoring.

Our Commitment to Diversity, Equity, Inclusionand Belonging (DEIB)

We believe attracting and retaining the best talent and fostering an inclusive culture strengthens our business. DEIB improves our workforce, enhances trust with our partners and customers, and drives business success. Build your career in a place where diversity, equity, inclusion and belonging aren’t just words on paper – this is what drives our innovation, it’s how we connect, and it contributes to what makes us a market leader. We believe in a hiring and working environment where all people are respected and valued, regardless of gender identity or expression, sexual orientation, religion, ethnicity, age, neurodiversity, disability status, citizenship, or any other aspect which makes people unique. We hire you for who you are, and we want you to bring your authentic self to work every day!

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions, and receive equitable benefits and all privileges of employment. Please contact us to request accommodation.