Senior MLOps / ML Platform Engineer

We are looking for a Senior MLOps Engineer to join Sigma Software and help build a production-grade ML platform for a large-scale AdTech ecosystem. You will work on infrastructure powering predictive decision-making systems that process hundreds of millions of auction requests daily.

The customer is a technology company operating supply-side infrastructure within the programmatic advertising ecosystem, managing a high-load ad exchange platform and investing in advanced predictive decision-making capabilities.

Sigma Software is building a predictive modeling and optimization platform integrated with a live ad exchange environment, combining large-scale ML infrastructure, automated model lifecycle management, multi-tenant architecture, and advanced observability practices.

Key Technologies: Python, Kubernetes, Docker, GCP, Vertex AI, MLflow, Airflow, Kubeflow, Argo Workflows, Terraform

• 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

Nice to have:

• 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

• 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