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Specialist - Machine Learning Ops

We are seeking a talented and experienced MLOps Engineer to join our team to help bridge the gap between machine learning development and production deployment. The ideal candidate will design, build, and maintain scalable ML pipelines and infrastructure to ensure reliable and efficient deployment of ML models into production environments.
 
 
Key Responsibilities:
Design and implement end-to-end MLOps pipelines (data ingestion, training, testing, deployment, monitoring).

  • Collaborate with Data Scientists and ML Engineers to productionize machine learning models.
  • Automate CI/CD for ML workflows using tools like GitHub Actions, Jenkins, or GitLab CI.
  • Deploy models using containers and orchestration platforms like Docker, Kubernetes, and SageMaker (or Vertex AI / Azure ML).
  • Manage model versioning, reproducibility, and drift detection using MLflow, DVC, or similar tools.
  • Monitor performance of deployed models in real time and implement alerts and retraining pipelines as needed.
  • Build and maintain scalable data processing workflows (e.g., using Spark, Airflow, or Prefect).
  • Ensure security, compliance, and governance of ML models in production.
  • Optimize model inference performance and resource utilization.

 
Requirements:

  • Bachelor's or Master's degree in Computer Science, Engineering, or related field.
  • 3+ years of experience in MLOps, DevOps, or ML Engineering roles.
  • Strong proficiency with cloud platforms (AWS, GCP, or Azure) and ML services.
  • Proficiency in Python and ML libraries like scikit-learn, TensorFlow, or PyTorch.
  • Experience with containerization and orchestration (Docker, Kubernetes).
  • Hands-on experience with ML workflow tools (e.g., MLflow, Kubeflow, TFX).
  • Familiarity with infrastructure-as-code (Terraform, CloudFormation).
  • Knowledge of monitoring/logging tools (Prometheus, Grafana, ELK Stack).
  • Experience in real-time data and model deployment (Kafka, Kinesis).
  • Exposure to data lake architectures (S3, Hive, Athena, BigQuery).
  • Understanding of security best practices for ML systems (model poisoning, data leakage prevention).
  • Familiarity with feature stores like Feast or Tecton.

Being you @ Almosafer:

At Almosafer, we strongly believe in diversity and equal opportunities for all candidates. We do not discriminate based on any characteristic and follow fair employment practices regarding citizenship and immigration status. Join our inclusive work environment.

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