Point Wild helps customers monitor, manage, and protect against the risks associated with their identities and personal information in a digital world. Backed by WndrCo, Warburg Pincus and General Catalyst, Point Wild is dedicated to creating the world’s most comprehensive portfolio of industry-leading cybersecurity solutions. Our vision is to become THE go-to resource for every cyber protection need individuals may face - today and in the future.

Join us for the ride! We’re looking for a Principal MLOps / Platform Engineer to architect and lead the infrastructure strategy for our next-generation Production ML platform on Google Cloud. In this role, you will be the backbone of our high-performance machine learning workloads, ensuring our systems are elastic, secure, and resilient.

You won’t just maintain the status quo; you’ll build the "paved road" for our engineers, automating everything from model deployment to complex networking perimeters. We are a high-trust, outcome-focused team that moves quickly to solve some of the most challenging problems in the ML space.

Responsibilities

: Infrastructure Management: Design, deploy, and maintain elastic scaling cloud infrastructure (GCP) and containerization tools like Kubernetes for high-performance ML workloads. CI/CD Pipeline Development and maintenance: Build automated pipelines for training, testing, and deploying machine learning models using tools like Jenkins, GitHub Actions, or Airflow. Model Monitoring Maintenance: Implement observability tools to track model drift, accuracy, latency, and performance degradation in production.

Collaboration: Bridge the gap between data engineers, ML engineers, Backend and Frontend engineers to ensure smooth production operation. ML Observability: Implement comprehensive monitoring for system health (latency/uptime) alongside ML-specific metrics, such as feature drift, prediction accuracy, and data distribution shifts, to ensure long-term model reliability. Non ML workload and production metrics monitoring.

Deploy tools that empower individual teams to monitor their workloads. Participate in on-call rotation, help manage posture to ensure compliance with standards such as SOC. What you bring to the table: Senior Expertise: Operating and maintaining production ML workloads, and DevOps/Platform Engineering.

, Triton, vLLM, MLflow). GCP K8s Mastery: Deep, hands-on experience with GCP (VPC-SC, IAM, Organization Policies) and GKE (Cluster topology, Helm, Kustomize, and in-cluster operators like ArgoCD). Service Mesh Excellence: High proficiency with Istio (VirtualServices, mTLS, sidecar injection) and API Gateways (specifically Kong ).

Infrastructure as Code: Expert-level Terraform skills, specifically using an Atlantis/GitOps workflow across a massive, multi-hundred-file estate. Secrets Identity: Experience managing enterprise-grade identity and secrets (Auth0, Dex, ESO, or SOPS). Database Management: Comfortable managing Cloud SQL (PostgreSQL), BigQuery, and in-cluster datastores like Elasticsearch or ClickHouse.

At least an upper-intermediate level of spoken and written English. It would be great if you also had: Automation Savvy: Experience with Ansible for cluster bootstrap and recovery. Advanced Certifications: Kubernetes (CKA/CKS) or GCP Professional Cloud Architect/Security Engineer certifications.

Modern Stack Exposure: Familiarity with Loki, Grafana , or managing ClickHouse at scale. As part of Point Wild, you will: Solve real customer problems . Point Wild’s point solutions allow consumers to address their immediate cyber protection needs.

Our mandate is to continuously anticipate our customers’ evolving digital security needs to create best-in-class solutions aimed at keeping them safe. See your impact. We are a scrappy, nimble organization where individual contributions are needed and valued.

You will see your impact every day.