Senior MLOps Engineer
NN Group is hiring a Senior MLOps Engineer in Madrid, Spain. Level rates it ; you can apply on NN Group.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- Tools and skills the posting cites include MLflow, Databricks and RAG.
- Level classifies it as a senior role.
- Level lists 170 other open roles at NN Group.
AI in this role
NN Digital Hub is a subsidiary company of NN Group located in Madrid, Spain. We deliver IT services and solutions for the different international Business Units.
Senior MLOps Engineer
As we scale our agentic AI strategy, we need to transform Databricks Mosaic AI into a self-service platform that empowers AI engineers across business units. Your role is to build and productize AI capabilities — from RAG pipelines to model serving — and deliver them as scalable, governed, and developer-friendly services.
The Data Intelligence Platform is the runtime for models and agentic AI at NN. It runs on Databricks MLflow and Mosaic, deploying models and agents on Databricks serverless or NN’s Kubernetes (Seldon) where needed. Mosaic Vector Search provides RAG for GenAI; the platform spans Azure and AWS to meet latency and proximity needs. As Senior MLOps Engineer, you will standardize build→deploy→serve→monitor across these topologies and make production operations audit-ready by design.
Your Impact as Senior MLOps Engineer: What are you going to do
Define the reference MLOps stack for the Agentic Platform: CI/CD patterns and environment promotion with approvals; serving topologies (Databricks serverless vs Kubernetes/Seldon) with decision records; evaluation & drift monitoring with MLflow/Mosaic; and SLO-based run operations. Integrate Unity Catalog lineage/policies into pipelines so teams meet governance requirements without friction and support the Run expansion.
Key responsibilities include:
Own CI/CD standards and environment promotion for models/agents: pipelines, approvals, artifact provenance, immutable releases, and rollback/canary patterns.
Standardize serving topologies: Databricks serverless vs Kubernetes/Seldon, with clear decision records, SLOs (latency/reliability), and cost/performance guardrails.
Implement evaluation and monitoring: MLflow-based offline/online evaluation, drift/quality checks, and end-to-end telemetry dashboards for model/agent behavior and costs with all security & compliance basic in orders in mind
Integrate governance-by-design: enforce Unity Catalog lineage/policies and capture approvals and evidence in pipelines to support audits and marketplace readiness.
Operate the Agentic Platform to SLOs: incident response/on-call, capacity planning, cost optimization, post-mortems, and continuous improvement of golden paths.
What do we offer?
As part of our Employee Experience, we offer you a range of competitive benefits that are available to you in order to improve the physical, mental, and professional wellbeing of our workers. Among the benefits are:
We work with a hybrid model
Starting financial aid so you can equip your workspace at home
Allowance and telework subventions
Flexible working hours and 2 months of intensive working hours in summer so you can fully take advantage of your time
We hire a life insurance and a pension plan for all our employees
We establish objectives bonus as a performance reward
You can get to the office as you please. We have a free parking lot for cars, motorbikes, electric cars with chargers, and bikes.
Get to know our flexible retribution facilities such as transport card, nursery checks, health insurance with Sanitas, trainings…
We care about people. That is why we are involved in society by facilitating volunteer actions and time to all our employees.
We care about your wellbeing. We have a Wellness Program available.
We are digital and we love technology. Also, every team works under agile methodology.
Who you are:
5+ years in MLOps/SRE or platform engineering with production ML/AI workloads; deep experience with Databricks MLflow is highly preferred and enterprise CI/CD.
Design/operation of multi-env releases (dev→test→prod) with approvals, secrets, and identity; IaC (Terraform/CrossPlane) and cloud networking on Azure and/or AWS.
Hands-on with evaluation/monitoring for models/agents; comfort with SLOs, incident response, and capacity/cost management.
Familiarity with tools like ArgoCD, Crossplane, Istio, Knative, Opensearch, Prometheus, and Grafana.
Optional qualifications:
Experience operating Kubernetes/Seldon for model serving and migrating toward Databricks Mosaic when appropriate.
Familiarity with agentic AI evaluation patterns (task success, tool reliability) and RAG observability (Vector Search health).
Experience in multi-cloud operations and cross-region latency management.
Working knowledge of Unity Catalog lineage/policies and how they integrate with delivery pipelines and catalogs; ability to produce audit-ready evidence.
If you are a passionate and experienced Senior MLOps Engineer looking for an exciting opportunity to work in a dynamic and challenging environment, we encourage you to apply for this position. We are waiting for you!
Please send your CVs in English language.
In Nationale-Nederlanden we are committed with diversity. We are proud to be an inclusive organization and we offer equal opportunities, regardless of race, cultural background, gender, gender identity, religion, national origin, age, disability, marital status, and sexual orientation. One of our core values is taking care of our employees so they can give their best within a respectful environment.
How we rate this
Senior MLOps Engineer at NN Group rates 88 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How do you decide that one model's output is better than another's for a given task?
- What's a project where you used MLflow hands-on?
- Walk me through how you've used Databricks in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: RAG, ML Ops, AI Evaluation, MLflow, and Databricks. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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