Senior MLOps, ML Platform Engineer

This is a fully remote position, open to applicants in Mali, +1 more country.

📋 Description

• Develop and sustain ML training orchestration pipelines scheduled on an hourly, daily, and weekly basis.

• Implement mechanisms for retries, backfills, and idempotent execution.

• Design and manage model registry workflows, including versioning, lineage tracking, evaluation gates, and promotion procedures.

• Create isolated model environments for individual advertisers, ensuring namespace and configuration separation.

• Construct scalable refresh pipelines and publishing workflows tailored for serving infrastructure.

• Execute shadow mode and champion/challenger deployment strategies.

• Develop monitoring and alert systems for ML-specific metrics such as feature drift, prediction drift, training/serving skew, and calibration decay.

• Guarantee the reproducibility of ML workflows through containerized environments, pinned dependencies, and data snapshots.

• Oversee training and scoring costs across different tenants.

• Partner with DevOps and SRE engineers to enhance CI/CD and infrastructure automation.

• Create operational documentation and materials for platform handover.

⛳️ Requirements

• A minimum of 5 years of experience in MLOps, ML platform engineering, or infrastructure engineering that supports production ML systems.

• Proficient in Python with a background in building platform-level tools and automation.

• Practical experience with Kubernetes and Docker.

• Familiarity in constructing CI/CD pipelines for ML workloads.

• Hands-on experience with MLflow, Kubeflow, Airflow, Argo Workflows, Vertex Pipelines, or comparable orchestration and ML lifecycle platforms.

• Experience with ML platforms and model lifecycle tools like Vertex AI, MLflow, or Kubeflow.

• Strong grasp of ML observability, including drift detection, monitoring train/serve skew, and incident response.

• Background in designing or supporting multi-tenant ML systems and isolated model environments.

• Experience with cloud platforms, ideally GCP.

• Knowledge of infrastructure-as-code tools such as Terraform.

• Proficiency in Linux environments.

• Understanding of the ML lifecycle and productionization processes.

• Upper-Intermediate English proficiency or better.

🏝️ Benefits

• Opportunity for remote work.

• Chance to engage in innovative ML infrastructure projects.

• Collaboration with seasoned engineers.

• Ability to influence architectural decisions.

• Long-term strategic engagement.