MLOps Engineer
Posted 21 Sep 2026
Advertised as “Senior MLOps Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 5+ yrs
- Management level
- Individual contributor
- Employment
- Remote
No salary listed for this role.
CertificationsGCP Professional Machine Learning Engineer, GCP Professional Cloud Architect
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!
About the Role:
As a Senior MLOps Engineer, you will play a critical role in architecting, building, and maintaining the infrastructure, pipelines, and tooling that enable complex AI models to be deployed, scaled, and monitored in production on Google Cloud Platform (GCP). You’ll collaborate closely with AI Researchers, Data Engineers, and Backend teams to bridge the gap between experimentation and high-performance, enterprise-grade production systems.
Your Day to Day:
- GCP ML Infrastructure: Architect and manage scalable GCP-based ML infrastructure using Vertex AI, Google Kubernetes Engine (GKE), Google Cloud Storage (GCS), Cloud Run, and GPU/TPU compute instances.
- Model Deployment & Serving: Own the end-to-end deployment lifecycle for machine learning models. Build high-throughput, low-latency inference services using containerization and specialized serving frameworks (e.g., Triton Inference Server, vLLM, MLflow).
- Continuous Integration & Training (CI/CD/CT): Build automated, reproducible pipelines for model training, testing, evaluation, and deployment using tools like Airflow, Vertex AI Pipelines, and GitHub Actions.
- Production Observability & Monitoring: Implement robust monitoring systems for both system health (latency, throughput, uptime) and ML-specific metrics (feature drift, prediction accuracy, and data distribution shifts) to enable automated retraining triggers.
- Supporting AI & Research Engineers: Provide scalable training environments, optimized runtime infrastructure, and standardized deployment templates that allow AI engineers to move fast without compromising reliability.
- Data & Feature Engineering Support: Collaborate with Data Engineers to integrate model pipelines with feature stores, dataset versioning, and stream/batch data processing workflows.
- Scaling PoCs to Production: Lead the technical transition of raw AI prototypes and notebooks into resilient, secure, and auto-scaling microservices.
What You Bring to the Table: