MLops Engineer
At a glance
Mid-level ML Platform & Ops role at Seedtag. Paris.
Pay not stated ML Platform & Ops salary
aimlroles summary, based on the employer's posting.
What you'll do
- Put machine-learning models into production, then keep their performance, reliability and response times on track
- Build data flows for creating datasets, training and validating models, and reviewing their performance
- Set up model-quality monitoring with dashboards, drift checks and alerts
- Strengthen CI/CD, model tracking and reproducibility while automating infrastructure and ML workflows
- Work with data, platform and backend teams to resolve production problems and improve serving efficiency
What you bring
- Two to four years building, deploying and supporting machine-learning systems in production
- Strong Python skills across APIs, asynchronous code, testing and maintainable architecture
- Practical experience developing ML models and putting them into production
- Working knowledge of MLOps, observability, Kubernetes and cloud infrastructure
Who this fits
This mid-level role suits an autonomous engineer who can take technical components from start to finish and troubleshoot models, data flows, APIs and infrastructure. Remote or hybrid work is available, with remote contracts offered in France and several other listed countries.
From the employer
We’re looking for an MLOps Engineer to join our tech team and help build the future of neuro-contextual advertising at global scale.
Who We Are
At Seedtag, our mission is to transform advertising by proving that effectiveness and user privacy can truly coexist.
As the leading Neuro-Contextual Advertising Company, we combine Artificial Intelligence, Natural Language Processing, Computer Vision, and neuroscience to understand not only what content is about, but how it makes people feel and what they intend to do next.
Our proprietary AI, Liz, enables brands to connect with audiences across the open web and Connected TV without cookies or user tracking. Founded in 2014 by two ex-Googlers, Seedtag has grown to 500+ Seedtaggers in 17 countries, backed by €250M in funding, and operates today as a global ad-tech leader.
If you enjoy solving complex engineering challenges and building AI-driven systems at scale, you’ll feel right at home here.
Your Challenge
As our MLOps Engineer, you will help scale and maintain the infrastructure behind Liz, Seedtag's contextual AI, ensuring reliable, efficient, and low-latency ML model deployment across the open web and CTV.
Your main responsibilities will include:
ML Model Deployment: Deploy, maintain, and optimise ML models in production, ensuring performance, reliability, and latency standards.
Data Pipelines: Build and improve pipelines for dataset generation, model training, validation, and performance analysis.
Model Observability: Implement monitoring, dashboards, drift detection, and alerting to ensure model quality and stability.
MLOps Infrastructure: Improve CI/CD pipelines, model versioning, reproducibility, and infrastructure automation.
Performance & Cost Optimisation: Identify opportunities to reduce serving costs and improve efficiency without compromising model quality.
AI Automation: Develop internal automations and agentic integrations to streamline ML workflows.
Cross-Functional Collaboration: Work closely with Data, Platform, and Backend teams to troubleshoot production issues and improve ML systems.
Our Core Values
Outcome over Output
We measure success by impact and value, not by volume of features or lines of code.
Failure Is Allowed, Learning Is a Must
Experimentation is key to innovation. We test early, iterate often, and learn fast.
We Are All Scouts
We take ownership and leave things better than we found them.
We Are Data-Driven
Data informs our decisions and helps us continuously improve our systems and results.
Tech Stack
You'll work with the technologies powering LizCore, our contextual AI infrastructure, supporting real-time ML model serving across the open web and CTV.
Our stack includes:
Languages: Python, Go.
Cloud & Infrastructure: GCP, Vertex AI, Kubernetes, Terraform.
MLOps & Monitoring: MLflow, Weights & Biases, Evidently, Prometheus, Grafana.
Data & Messaging: Kafka, Redis, Bigtable, MongoDB.
AI & Machine Learning: NLP, embeddings, ranking models, MCP, agentic workflows.
Engineering Practices: CI/CD, model versioning, automated testing, reproducibility.
What You’ll Need to Succeed
2–4 years of experience building, deploying, and maintaining ML systems in production.
Strong Python skills, including APIs, asynchronous programming, testing, and clean architecture.
Hands-on experience with ML model development and production deployment.
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.
Strong troubleshooting skills across models, data pipelines, APIs, and infrastructure.
Proactive, curious, and autonomous, with the ability to own technical components end to end.
Why Join Seedtag?
A key moment of growth with real ownership and global impact.
Flexible work model with 100% remote or hybrid options.
(Remote contracts available in Spain, Italy, UK, Belgium, Netherlands, France, and Germany.)Continuous learning through a learning platform and optional language classes.
A supportive, trust-based culture that values well-being.
Team activities, offsites, and opportunities to connect beyond work.
Additional Perks
Home office setup budget up to €1,000
Paid trips to our HQ in Madrid
MacBook Pro M3
Ready to Join the Seedtag Adventure?
At Seedtag, we create an environment where everyone can thrive. If you need accommodations during the hiring process, let us know and we’ll ensure a positive experience.
Send us your CV and let’s build the future of neuro-contextual advertising together.