Senior MLOps Engineer

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🔥 0 minutes ago

🌐 Ukraine, Poland – Remote

🇺🇦 Ukraine – Remote 🇵🇱 Poland – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Point Wild (Formerly Pango Group)

WebsiteLinkedInAll Job Openings

51 - 200 employees

🔒 Cybersecurity

☁️ SaaS

🏢 Enterprise

Cybersecurity • SaaS • Enterprise

Point Wild is a leader in online security, providing innovative solutions for consumer identities and endpoint protection. The company focuses on delivering robust security technologies that safeguard both individual users and enterprises from data breaches and privacy incidents. Through its various brands, Point Wild offers services such as VPN and Antivirus solutions, which have been widely adopted and downloaded by millions, ensuring privacy and protection in today's digital landscape.

📋 Description

• 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 • Own the end-to-end deployment lifecycle for machine learning models • Build high-throughput, low-latency inference services using containerization and specialized serving frameworks such as Triton Inference Server, vLLM, and MLflow • Build automated, reproducible pipelines for model training, testing, evaluation, and deployment using Airflow, Vertex AI Pipelines, and GitHub Actions • Implement monitoring for system health and ML-specific metrics, including latency, throughput, uptime, feature drift, prediction accuracy, and data distribution shifts • Provide scalable training environments, optimized runtime infrastructure, and standardized deployment templates for AI engineers • Collaborate with Data Engineers on feature stores, dataset versioning, and stream/batch data processing workflows • Lead the transition of AI prototypes and notebooks into resilient, secure, auto-scaling microservices • Collaborate with AI Researchers, Data Engineers, and Backend teams to bridge experimentation and production systems

🎯 Requirements

• At least 5 years of hands-on experience designing, deploying, and maintaining production ML workloads in cloud environments • Deep, practical experience with Google Cloud Platform (GCP), including Vertex AI, Cloud Storage, GKE, Cloud Run, and IAM/VPC configurations • Expertise with containerization (Docker, Kubernetes/GKE) and specialized serving tools (Triton, vLLM, MLflow) • Proven track record with workflow orchestrators (Airflow, Vertex AI Pipelines) and modern CI/CD tools (GitHub Actions, ArgoCD) • Solid experience managing cloud resources using Terraform • Proficiency in Python and SQL for scripting, automation, API development, and data manipulation • Hands-on experience with logging, telemetry, and drift detection tools (Grafana, Prometheus, GCP Cloud Monitoring, or specialized ML observability frameworks) • 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 such as Feast or Vertex AI Feature Store

🏖️ Benefits

• Opportunity to learn new technologies, products, and markets in a fast-paced, growth-oriented environment • Work with talented people at a company where people matter • Inclusive community and commitment to a discrimination- and harassment-free workplace