Senior MLOps Engineer

• 5+ years inMLOps/SRE or platform engineering with production ML/AI workloads; deep experience with DatabricksMLflowis highly preferredand 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 likeArgoCD,Crossplane, Istio,Knative,Opensearch, Prometheus, and Grafana.

• Experience operating Kubernetes/Seldon for model serving and migrating toward Databricks Mosaic whenappropriate.

• 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.

As wescaleour 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 DatabricksMLflowand 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. AsSeniorMLOpsEngineer, you will standardizebuild→deploy→serve→monitoracross these topologies and make production operations audit-ready by design.

Your Impact as SeniorMLOpsEngineer: What are you going to do

Define the referenceMLOpsstack 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 withMLflow/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.

• 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, withclear 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.