ML Ops Engineer

Overview

In this ML Ops Engineer role, you will design, scale, and maintain high-performance AI/ML infrastructure to support Anaplan’s AI-infused scenario planning platform. You’ll collaborate with data scientists, ML engineers, and cloud teams to optimize training, deployment, and inference workflows with a focus on GPU efficiency and cost control.

You’ll build robust CI/CD and MLOps pipelines, deploy LLMs, and ensure observability and reliability at scale. This role offers the opportunity to shape critical AI infrastructure for a market-leading analytics platform and drive performance and cost efficiency in production.

A clear hook is the chance to impact decision-making for Fortune 50 customers by

Responsibilities

• Provision and manage cloud-native AI/ML infrastructure using Kubernetes, Docker, and GPU orchestration frameworks (e.g., NVIDIA GPU Operator, Slurm, Ray)
• Automate platform infrastructure with Infrastructure as Code tools (Terraform, Helm, Ansible)
• Optimize GPU compute workloads, networking, and storage for training and low-latency inference
• Build and maintain CI/CD and MLOps pipelines for model training, evaluation, packaging, and deployment
• Deploy LLMs and generative AI workloads using inference engines (e.g., Triton Inference Server, vLLM, TensorRT-LLM)
• Enable automated model validation and monitoring for drift and latency Bottlenecks
• Monitor and optimize cloud spend across AWS, GCP, or Azure; implement auto-scaling and spot policies
• Establish benchmarking and telemetry for training/serving throughput and unit economics
• Implement end-to-end observability with Prometheus, Grafana, OpenTelemetry, and MLflow/Weights & Biases

Key requirements

• Hands-on production experience in DevOps, SRE, or Platform Engineering with AI/ML infrastructure focus
• Proven track record deploying and scaling ML models and LLMs in production
• Experience managing GPU infrastructure and HPC environments
• Advanced proficiency in Kubernetes, Docker, Helm, KubeFlow, 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 optimization
• Strong skills in Python, Bash, or Go; knowledge of Linux kernel tuning and performance monitoring
• collaboration
• analytical thinking
• problem-solving
• Kubernetes
• Docker
• Terraform

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