MLOps Engineer — Remote, Scale AI for Global AdTech

Seedtag; España; Remote; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Home office budget up to €1,000

Paid trips to HQ in Madrid

MacBook Pro M3

Job summary

Seedtag is seeking an MLOps Engineer to scale and maintain the infrastructure behind Liz, Seedtag's contextual AI, delivering reliable, low-latency model deployment across the open web and CTV.

You will deploy and monitor ML models, build data pipelines, and improve CI/CD and observability while collaborating with Data, Platform, and Backend teams to ensure robust ML systems. Remote or hybrid options are available, with international contracts.

Qualifications

• 2-4 years of experience building, deploying, and maintaining ML systems in production.
• Strong Python skills, including APIs, asynchronous programming, testing, and clean architecture.
• Solid understanding of MLOps practices, including CI/CD, model versioning, reproducibility, and monitoring.
• Experience with ML observability, including performance metrics, dashboards, and alerting.
• Knowledge of distributed systems, microservices, Kubernetes, and cloud infrastructure.

Responsibilities

• Deploy, maintain, and optimise ML models in production with low latency.
• Build and improve data pipelines for dataset generation and model training.
• Implement monitoring, dashboards, drift detection, and alerting for model quality.
• Improve CI/CD pipelines, model versioning, reproducibility, and infrastructure automation.
• Identify opportunities to reduce serving costs while preserving model performance.
• Develop internal automations and agentic integrations to streamline ML workflows.
• Collaborate with Data, Platform, and Backend teams to troubleshoot production issues.

Skills

Python

Go

Tools

Kubernetes

Terraform

MLflow

Weights & Biases

Evidently

Prometheus

Grafana

Kafka

Redis

Bigtable

MongoDB

Vertex AI

Job description

Seedtag is seeking an MLOps Engineer to scale and maintain the infrastructure behind Liz, Seedtag's contextual AI, delivering reliable, low-latency model deployment across the open web and CTV.

You will deploy and monitor ML models, build data pipelines, and improve CI/CD and observability while collaborating with Data, Platform, and Backend teams to ensure robust ML systems. Remote or hybrid options are available, with international contracts.