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 operating on hourly, daily, and weekly schedules

• Implement mechanisms for retries, backfills, and idempotent execution

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

• Create isolated model environments for each advertiser, ensuring namespace and configuration separation

• Construct scalable refresh pipelines and publishing workflows for the serving infrastructure

• Execute shadow mode and champion/challenger deployment strategies

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

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

• Track training and scoring expenditures across different tenants

• Collaborate with DevOps and SRE engineers to enhance CI/CD and automate infrastructure

• Create operational documentation and prepare platform handover materials

⛳️ Requirements

• Over 5 years of experience in MLOps, ML platform engineering, or infrastructure engineering supporting production ML systems

• Proficient in Python with experience in building platform-level tools and automation

• Practical experience with Kubernetes and Docker

• Proven experience in developing CI/CD pipelines for ML workloads

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

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

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

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

• Background in working with cloud platforms, preferably GCP

• Familiarity with infrastructure-as-code tools such as Terraform

• Experience in Linux environments

• Understanding of the ML lifecycle and productionization processes

• Upper-Intermediate English proficiency or higher

🏝️ Benefits

• Option for remote work

• Opportunity to engage in innovative ML infrastructure projects

• Collaboration with skilled engineers

• Ability to influence architectural decisions

• Long-term strategic engagement