Senior MLOps / ML Platform Engineer
Core
Build and maintain scheduled ML training orchestration pipelines, model registry workflows, and scalable multi-tenant ML infrastructure for an AdTech ecosystem.
Role type
Senior MLOps / ML Platform Engineer
Builds
Production ML systems, multi-tenant model environments, and automated model lifecycle workflows
Domain
AdTech, Cloud Infrastructure, Machine Learning Operations
Deliverable
production ML models | infrastructure
Required skills
Python, Kubernetes, Docker, CI/CD for ML, ML orchestration platforms (MLflow, Kubeflow, Airflow, Argo Workflows, Vertex Pipelines), Cloud platforms (GCP), Infrastructure-as-Code (Terraform), Linux, ML observability, drift detection, multi-tenant system design
Preferred skills
Feature stores, large-scale batch scoring, experiment tracking, evaluation gates, on-premises Kubernetes, bare-metal Linux, DVC, lakeFS, Bigtable, Redis, Aerospike, GPU scheduling, training cost optimization, compliance (SOC 2, ISO 27001, GDPR)
Technologies
Kubernetes, Docker, MLflow, Kubeflow, Airflow, Argo Workflows, Vertex Pipelines, Terraform, GCP, Linux, Bigtable, Redis, Aerospike, DVC, lakeFS
Responsibilities
Build and maintain scheduled ML training orchestration pipelines with retries and backfills; Develop model registry, versioning, lineage, and promotion workflows; Design isolated per-advertiser model environments and scalable multi-tenant ML infrastructure; Implement shadow-mode and champion/challenger deployment strategies; Develop ML observability for feature drift, prediction drift, and operational incidents; Monitor training and scoring costs across tenants; Collaborate with DevOps and SRE engineers on CI/CD and infrastructure automation
Seniority
Senior, hands-on IC
