Senior ML Engineer - End-to-End Production AI Lead

Cph Ai Hub

København

Hybrid

DKK 651.000 - 957.000

Vollzeit

14 Tage+

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

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Zusammenfassung

Novo in Denmark is seeking a Senior Machine Learning Engineer to turn models into reliable production services. You will design, deploy and operate ML, generative AI and agent-based apps on Databricks, AWS and Azure, setting engineering practices for dependable tools used daily.

You will own end-to-end ML lifecycle, work with data scientists and software engineers, and mentor colleagues while ensuring secure, observable and cost-efficient deployments.

Qualifikationen

  • Degree in computer science, engineering, mathematics or related field or equivalent experience.
  • Typically 5+ years in ML engineering or related roles with production ML ownership.
  • Strong Databricks experience and Python-based ML development.
  • Experience deploying ML on AWS/Azure with CI/CD and IaC tooling.

Aufgaben

  • Design, build, deploy and maintain ML models, LLM apps and AI agents.
  • Create training, evaluation, deployment and monitoring workflows.
  • Establish MLOps, LLMOps and DataOps practices with governance.
  • Monitor quality, data drift, latency and cost; investigate failures.
  • Lead design discussions, review code and mentor engineers.

Kenntnisse

Databricks
Python
PySpark
MLflow
Delta Lake
AWS
Azure
MLOps
LLMOps
DataOps

Ausbildung

Bachelor's degree in Computer Science or related field

Tools

Git
CI/CD
Terraform
Kubernetes

Jobbeschreibung

Novo in Denmark is seeking a Senior Machine Learning Engineer to turn models into reliable production services. You will design, deploy and operate ML, generative AI and agent-based apps on Databricks, AWS and Azure, setting engineering practices for dependable tools used daily.

You will own end-to-end ML lifecycle, work with data scientists and software engineers, and mentor colleagues while ensuring secure, observable and cost-efficient deployments.

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