MLOps Engineer - Agentic Platform
Madrid
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s listing: Full time
From the employer’s posting
What you’ll work on
Full postingOwn CI/CD pipelines, approvals and environment promotion for models and agents
Standardize serving on Databricks serverless or Kubernetes/Seldon with decision records
Enforce Unity Catalog lineage and policies in pipelines to support audits
Run the Agentic Platform to SLOs, including on-call, capacity and cost work
From the employer’s posting
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
Integrate governance-by-design: enforce Unity Catalog lineage/policies and capture approvals and evidence in pipelines to support audits
Operate the Agentic Platform to SLOs: incident response/on-call, capacity planning, cost optimization, post-mortems
Tools in this posting
- AWS
- Azure
- Databricks
- Grafana
- Kubernetes
- MLflow
Source — Tool mentions in context
- 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.
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
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.
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).
- 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:
- 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.
Posting record
Report an errorOpen 3 monthsWe first saw this listing 105 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jul 9, 2026 · 105 days ago
- Recorded sightings
- 187
- Last seen by us
- Oct 23, 2026
- Employer says posted
- Feb 23, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
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Job description
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.
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Madrid
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
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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