Senior MLOps Engineer

Senior MLOps EngineerFully remote in either Poland, Ukraine, Romania, Bulgaria, Latvia, Lithuania or EstoniaPoint 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:Senior MLOps Experience: At least 5 years of hands-on experience designing, deploying, and maintaining production ML workloads in cloud environments.GCP Ecosystem Mastery: Deep, practical experience with Google Cloud Platform (GCP), including Vertex AI, Cloud Storage, GKE, Cloud Run, and IAM/VPC configurations.Model Serving & Tooling: Expertise with containerization (Docker, Kubernetes/GKE) and specialized serving tools (Triton, vLLM, MLflow).Orchestration & CI/CD: Proven track record with workflow orchestrators (Airflow, Vertex AI Pipelines) and modern CI/CD tools (GitHub Actions, ArgoCD).Infrastructure as Code (IaC): Solid experience managing cloud resources using Terraform.Software Development Skills: Proficiency in Python and SQL for scripting, automation, API development, and data manipulation.ML Observability: Hands-on experience with logging, telemetry, and drift detection tools (Grafana, Prometheus, GCP Cloud Monitoring, or specialized ML observability frameworks).Nice to Have:Experience running large-scale LLM or Deep Learning inference/training workloads.GCP Professional Machine Learning Engineer or GCP Professional Cloud Architect certifications.Familiarity with feature stores (e.g., Feast, Vertex AI Feature Store). Why This Role Matters:Operationalizing AI: AI models only create value when they operate reliably at scale. You are the architect making production AI possible.Infrastructure Backbone: You provide the core practices, automation, and tooling that empower AI teams to innovate rapidly while maintaining system stability.Cross-Functional Bridge: You connect the worlds of data science, cloud operations, and software engineering to maintain production reliability.