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NN Group

MLOps Engineer - Agentic Platform

NN Group is hiring a MLOps Engineer - Agentic Platform in Madrid, Spain. Level rates it ; you can apply on NN Group.

Level's read on this job

  • Little AI ●○○○ is how Level rates this role: aI is not part of the work.
  • Tools and skills the posting cites include MLflow, Databricks and RAG.
  • Level lists 169 other open roles at NN Group.

AI in this role

mlflowdatabricks
ragml-opsai-evaluation

NN Digital Hub is a subsidiary company of Nationale Nederlanden Group located in Madrid, Spain. We deliver IT services and solutions for the different international Business Units from Nationale Nederlanden Group.

Our objective is to create, deliver and maintain insurance product management solutions, as service propositions to enable any agent (linked or direct), machine or company to create and sell insurance products in Europe in an automated way and real time.

We are looking for an experienced MLOps Engineer to join our team.

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.

Your impact at NN: What you´ll 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. 

What are the tasks that you’ll perform?

  • 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 work space 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 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. 

Nice to have: 

  • 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 think that your profile can fit with us and you like who we are, do not hesitate to sign in to our offers. We are waiting for you!

Please send your CVs in English language.

At NN, we believe our strength lies in embracing the full spectrum of humanity. We recognise each person’s uniqueness and contributions which empowers us to succeed as individuals, as teams, and as a company. Diversity, Equity, and Inclusion is embedded in our culture which is guided by our values — care, clear, commit — and supports us in fostering a workplace where everyone feels welcome, respected, and proud to work. 

Be you. You have our support.

How we rate this

MLOps Engineer - Agentic Platform at NN Group rates 15 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ 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

RAGML OpsAI EvaluationMLflowDatabricks

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you monitor a model once it's live, and how do you know it needs retraining?
  3. How do you decide that one model's output is better than another's for a given task?
  4. What's a project where you used MLflow hands-on?
  5. 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.

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