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Senior MLOps Engineer

cander · Abou Dabi

دائم Onsite Senior 🇬🇧 English
Kubernetes Terraform CI/CD KServe Seldon BentoML vLLM Triton MLflow Kubeflow Airflow Prometheus Grafana Python AWS Azure GCP

وصف الوظيفة

About the role

AI71, an enterprise technology company based in Abu Dhabi that builds AI infrastructure, is hiring a Senior MLOps Engineer for its Platform Engineering team. The role builds the foundational layers that deploy, monitor and maintain models for enterprise customers, bridging model development and production. It is a platform engineering position focused on reliability, security and scalability, with an on-site arrangement in Abu Dhabi (hybrid option to be confirmed).

Key responsibilities

  • Design and build the model serving layer, including real-time inference, batch processing and LLM endpoints.
  • Develop CI/CD pipelines for models covering automated training, testing, versioning, staged rollouts and rollback mechanisms.
  • Set up observability to track performance, model drift, data quality, cost and output quality.
  • Own the evaluation infrastructure, including regression testing for non-deterministic outputs.
  • Maintain infrastructure as code across all environments, meeting security and compliance standards.
  • Work with data scientists and ML engineers to move from notebooks to production and define architectural standards and internal tooling.

Required profile

  • Substantial hands-on experience running ML systems in production, with a focus on platform engineering.
  • Strong software engineering skills in Python, with maintainable production code.
  • Judgment to balance platform complexity with the company's stage, avoiding both fragile scripts and over-engineered systems.
  • Collaborative approach to working with data scientists and ML engineers.

Required skills

  • Kubernetes and containerization, including cluster-level architecture decisions.
  • Infrastructure as code (Terraform or equivalent) and end-to-end CI/CD ownership.
  • Model serving frameworks such as KServe, Seldon, BentoML, vLLM or Triton.
  • ML lifecycle tooling such as MLflow, Kubeflow, Airflow or feature stores.
  • Monitoring and observability with Prometheus, Grafana or similar.
  • Cloud platforms (AWS, Azure, GCP or sovereign clouds).

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cander

Abou Dabi