Senior MLOps Engineer

Job not on LinkedIn

🔥 5 minutes ago

🇷🇴 Romania – 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, Google Cloud Storage, 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 • Build automated, reproducible pipelines for model training, testing, evaluation, and deployment • Implement monitoring for system health and ML-specific metrics, including automated retraining triggers • 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, including Vertex AI, Cloud Storage, GKE, Cloud Run, and IAM/VPC configurations • Expertise with containerization using Docker and Kubernetes/GKE • Expertise with specialized serving tools including Triton, vLLM, and MLflow • Proven track record with Airflow, Vertex AI Pipelines, GitHub Actions, and 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 such as Grafana, Prometheus, GCP Cloud Monitoring, or specialized ML observability frameworks • Experience running large-scale LLM or deep learning inference/training workloads is nice to have • GCP Professional Machine Learning Engineer or GCP Professional Cloud Architect certification is nice to have • Familiarity with feature stores such as Feast or Vertex AI Feature Store is nice to have

🏖️ Benefits

• Opportunity to learn new technologies, products, and markets in a fast-paced, growth-oriented environment • Work with talented people at an inclusive company where people matter • Individual contributions are valued and employees can see their impact