Senior MLOps ML Platform Engineer

• Build and maintain ML training orchestration pipelines across hourly daily and weekly schedules
• Implement retries backfills and idempotent execution mechanisms
• Design and support model registry workflows including versioning lineage evaluation gates and promotion processes
• Develop isolated per-advertiser model environments with namespace and configuration separation
• Build scalable refresh pipelines and publishing workflows for serving infrastructure
• Implement shadow mode and champion/challenger deployment strategies
• Develop monitoring and alerting for ML-specific metrics including feature drift prediction drift train/serve skew and calibration decay
• Ensure reproducibility of ML workflows using containerized environments pinned dependencies and data snapshots
• Monitor training and scoring costs across tenants
• Collaborate with DevOps and SRE engineers on CI/CD and infrastructure automation
• Prepare operational documentation and platform handover materials

Qualifications :

• 5 years of experience in MLOps ML platform engineering or infrastructure engineering supporting production ML systems
• Strong Python skills and experience building platform-level tooling and automation
• Hands-on experience with Kubernetes and Docker
• Experience building CI/CD pipelines for ML workloads
• Hands-on production experience with MLflow Kubeflow Airflow Argo Workflows Vertex Pipelines or similar orchestration and ML lifecycle platforms
• Experience with ML platforms and model lifecycle tools such as Vertex AI MLflow or Kubeflow
• Strong understanding of ML observability including drift detection train/serve skew monitoring and incident response
• Experience designing or supporting multi-tenant ML systems and isolated model environments
• Experience working with cloud platforms preferably GCP
• Experience with infrastructure-as-code tools such as Terraform
• Experience with Linux environments
• Understanding of the ML lifecycle and productionization processes
• Upper-Intermediate English level or higher

WILL BE A PLUS

• Experience with feature stores and feature consistency management
• Experience with large-scale batch scoring systems operating under freshness SLAs
• Familiarity with experiment tracking platforms and evaluation gates
• Experience with on-premises Kubernetes or bare-metal Linux infrastructure
• Knowledge of DVC lakeFS or other data versioning tools
• Experience with Bigtable Redis Aerospike or similar low-latency serving databases
• GPU scheduling and training cost optimization experience
• Familiarity with SOC 2 ISO 27001 or GDPR-related compliance requirements

Additional Information :

PERSONAL PROFILE

• Strong ownership mindset and focus on operational reliability
• Ability to work independently in complex distributed systems environments
• Strong collaboration and communication skills
• Analytical thinking with attention to scalability and maintainability
• Comfortable working in fast-paced product-oriented environments

Remote Work :

Yes

Employment Type :

Full-time