Full-Time Senior MLOps Engineer

• Empowering ML Engineers by providing tools and infrastructure that enhance their productivity while adapting to their specific needs.
• Accelerating time-to-market for the first production-ready versions of ML products by ensuring seamless integration, proper service connections, and access to data and resources.
• Ensuring ML Engineers maintain control over their models in production, allowing them to monitor, troubleshoot, iterate quickly, and refine models and feature engineering logic.
• Facilitating large-scale ML experimentation by providing robust, reproducible, and scalable test environments for data science projects, enabling both qualitative experimentation (internal tests and demos) and quantitative experimentation (A/B testing in production).
• Owning the industrialization and deployment (CI/CD) of ML models, ensuring their reliability, scalability, and performance through advanced MLOps practices.
• Managing model operations and maintenance, guaranteeing optimal performance, monitoring, and continuous improvements in production.

• 3+ years of experience in MLOps, DevOps, or Data Engineering.
• Strong Expertise with cloud platforms (GCP,AWS,Azure) their ML services (Vertex AI, SageMaker, etc.) and data services ( Bigquery, buckets )
• Strong Expertise in CD pipelines for ML models (Flux)
• Experience in ML Orchestrators ( Kubeflow, Kuberay )
• Experience in CI pipelines for ML models (Flux, Jx3)
• Experience in Data pipelines for ML models (Airflow,DataFlow,Kestra)
• Experience with Data lifecycle Management (Storage policies, costs optimization , security, encryption).
• Proficiency in containerization and orchestration (Docker, Kubernetes, Helm).
• Experience in ML infrastructure optimization (GPUs,TPUs, Inferentia).
• Expertise with Infrastructure as Code (Terraform).
• Expertise in monitoring and observability capabilities (Prometheus, Datadog, Looker)
• Strong programming skills in Python, with experience in Bash scripting; knowledge of Go or Rust is a plus.

• Strong collaboration and communication skills to work effectively with ML Engineers and cross-functional teams.
• Ability to drive innovation and advocate for best MLOps practices within the company.
• Excellent English communication skills, capable of working effectively in an international team.

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