SENIOR MLOPS /AIOPS ENGINEER

• CDI
• Casablanca
• Publié il y a 1 an

RED TIC is recruiting a skilled MLOps/AIOps Engineer to join our team. In this role, you will bridge the gap between machine learning model development and production deployment. You will work with cross-functional teams (data scientists, software engineers, DevOps engineers) to ensure scalable, efficient, and robust machine learning models are delivered into our environments.

Required Skills

• Automate and streamline the deployment of machine learning models into production systems. Ensure that ML models are properly integrated with applications, services, and infrastructure.
• Build and maintain CI/CD pipelines for machine learning models, enabling rapid experimentation and iteration while ensuring quality and performance in production.
• Leverage priority and open-source technologies to support CI/CD pipelines.
• Establish and maintain robust monitoring systems to track model performance in production, ensuring continuous evaluation and early detection of issues.
• Implement real-time performance metrics to evaluate key indicators such as accuracy, latency, and resource usage, ensuring models meet business objectives and user needs.
• Set up monitoring for model performance in production and address any issues that arise. This includes performance degradation, model drift, and other production challenges.
• Work closely with data scientists and architects to understand model requirements and deployment constraints. Collaborate with DevOps, software engineers, and other stakeholders to ensure a seamless transition from model development to production.
• Design and manage infrastructure for training and serving models at scale. This might include cloud resources (AWS, Azure, GCP, OCI), containerization (Docker, Rancher, Kubernetes), and orchestration tools.
• Build robust data pipelines for training and testing models. Automate the entire machine learning lifecycle from data preprocessing to model serving and monitoring.
• Ensure that models, datasets, and experiments are versioned and reproducible. Implement version control for models and maintain an effective model registry.

Qualifications

• 2+ years of experience in MLOps, DevOps, or software engineering, with at least one year focused specifically on machine learning model deployment.
• Proficiency in Python (most common for ML workflows) and experience with languages such as Java, or Go is a plus.
• Familiarity with popular machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) and tools such as MLflow, Kubeflow, or TFX.
• Experience working with cloud platforms like AWS, GCP, Azure or OCI. Proficiency in containerization technologies (Docker, Rancher) and orchestration (Kubernetes, Helm).
• Experience with CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions) and Git.
• Familiarity with tools for model monitoring, logging, and observability (e.g., Prometheus, Grafana,).
• Strong understanding of data pipeline design, and data storage technologies (e.g., SQL, Object Storage).
• Strong communication skills to work with cross-functional teams .
• Familiarity with distributed systems and big data frameworks like Hadoop, Spark, etc.
• Experience in automating machine learning (ONNX) or model training optimization.
• Experience in model interpretability, explainability, and fairness.

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Nom et Prénom

Adresse Email

Mobile

Niveau d étude

Bac Bac +1 Bac +2 Bac +3 Bac +4 Bac +5 Bac + X

Années d’Expérience

0 +1 +2 +3 +4 +5 +6 +7 +8 +9 + 10

Préavis

Disponible immédiatement -1 mois 1 mois 2 mois +2 mois

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