Senior MLOps Engineer: Scalable AI Infra on GCP

pointwild; United States; Remote; USD 140,000 - 220,000; Full time

Benefits offered by this job

Opportunity to influence cybersecurity

Enterprise-scale production AI

Job summary

pointwild seeks a senior MLOps engineer to build production ML infrastructure on Google Cloud, turning notebooks into resilient, auto-scaling services. You will work with researchers, data engineers and backend teams to architect pipelines and tooling.

Applicants should have 5+ years deploying ML workloads in the cloud, strong GCP expertise, and hands-on experience with Docker/Kubernetes, Airflow, Vertex AI Pipelines and CI/CD.

Qualifications

• Five+ years of hands-on production ML in cloud environments.
• GCP expertise: Vertex AI, Cloud Storage, GKE, Cloud Run, IAM/VPC.
• Containerisation with Docker and Kubernetes; specialised serving tools (Triton, vLLM, MLflow).
• Workflow orchestration with Airflow or Vertex AI Pipelines; CI/CD with GitHub Actions or ArgoCD.
• Terraform for cloud resource management.
• Python and SQL for scripting, automation and data handling.
• Logging/telemetry via Grafana, Prometheus, GCP Monitoring or ML observability tools.

Responsibilities

• Architect and manage scalable GCP-based ML infra (Vertex AI, GKE, Cloud Storage, Cloud Run, GPU/TPU).
• Own end-to-end deployment lifecycle; build high-throughput, low-latency inference services.
• Create automated, reproducible pipelines for training, testing, evaluation and deployment (Airflow, Vertex Pipelines, GitHub Actions).
• Monitor system health and ML metrics; implement automated retraining triggers.
• Provide scalable training environments and standardised deployment templates for researchers.
• Collaborate with data engineers to integrate feature stores, versioning and streaming/batch workflows.
• Lead migration of prototypes/notebooks into resilient, secure microservices.

Skills

GCP Vertex AI

Docker

Kubernetes

CI/CD

Terraform

Python

SQL

Grafana

Prometheus

Airflow

Vertex AI Pipelines

MLflow

Triton

vLLM

Tools

GKE

Cloud Run

GitHub Actions

ArgoCD

Cloud Storage

IAM/VPC

Job description

pointwild seeks a senior MLOps engineer to build production ML infrastructure on Google Cloud, turning notebooks into resilient, auto-scaling services. You will work with researchers, data engineers and backend teams to architect pipelines and tooling.

Applicants should have 5+ years deploying ML workloads in the cloud, strong GCP expertise, and hands-on experience with Docker/Kubernetes, Airflow, Vertex AI Pipelines and CI/CD.