MLOps Engineer

Role: MLOps Engineer
Job Ref Id: JD-1283
Competence level: 5+ years
Start date: 2026-12-01
End date: 2027-06-01
Extension option: Yes
Scope: 100%
Location: Stockholm
Remote work: Remote
Language: English
Application deadline: 2026-11-01
Number of consultants: 1
Security clearance: No

• ML platform on GCP: Vertex AI, Kubeflow Pipelines and GKE-based training and serving infrastructure, kept reliable, secure and cost-efficient.
• Paved roads for ML teams: templates, CI/CD and self-serve tooling so data scientists ship models without filing tickets.
• Model lifecycle: registry, versioning, deployment, rollback, and monitoring for drift and serving health.
• Data integration: feature and training data flows across BigQuery, Airflow together with the data platform engineers.
• Infrastructure as code: everything managed through GitOps with Config Sync, with access following least privilege for PII and policy-tagged data.
• Cost and capacity: GPU/compute usage, quotas, and spend visibility per team.

• 5+ years in MLOps, ML platform or platform engineering, with at least 2 years owning production ML infrastructure end to end.

• Hands-on GCP: Vertex AI, GKE and BigQuery in production, not only as skill tags.

• Kubernetes depth: you've run and debugged clusters yourself, not just deployed to them.

• Pipeline orchestration with Kubeflow Pipelines or Vertex AI Pipelines, plus Airflow.

• GitOps for infrastructure (Config Sync or similar) and CI/CD for model deployment.

• Python to production standard, including tests and packaging.

• Proven platform thinking: you've built tooling other teams self-serve on, with docs and SLOs.

• Comfortable working independently as the single owner of a company-wide platform.

• Streaming features or online inference with Dataflow/Flink and Kafka.

• Feature stores (Vertex AI Feature Store, Feast) and model monitoring tooling.

• LLM serving and evaluation on Vertex AI or similar.

• GPU scheduling and cost optimisation on GKE.

• Experience with data governance (DataHub, policy tags) and privacy regulation such as GDPR or DPDP.

• Prior experience in high-scale consumer products.

Originally posted on Himalayas