Senior MLOps / ML Platform Engineer
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Summary
Senior engineer building and operating an ML platform: training orchestration pipelines, model registry and promotion workflows, shadow/champion-challenger deployments, drift and skew monitoring, and isolated multi-tenant model environments. Core stack: Python, Kubernetes, Docker, MLflow/Kubeflow/Airflow/Argo, GCP, and Terraform.
- 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
- 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
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