Machine Learning Engineer - 5
Core
Manage end-to-end ML workflows, model lifecycle, and platform infrastructure for an advertising cloud search and optimization platform.
Role type
Senior MLOps/Platform Engineer
Builds
Scalable ML pipelines, model registries, CI/CD orchestration, and production monitoring systems for digital marketing optimization.
Domain
Digital Marketing / Advertising Cloud / Cloud Infrastructure
Deliverable
production ML models | infrastructure
Required skills
Cloud architecture design (AWS), MLOps frameworks (MLflow, Kubeflow, Airflow), Containerization (Docker, Kubernetes), Python programming, Software engineering best practices, Observability tools, Infrastructure as Code (Terraform)
Preferred skills
Go/Ruby/Bash scripting, Feature stores (Feast, Tecton), Cloud ML services (SageMaker, Vertex AI), Model governance & lineage tools
Technologies
AWS, GitLab CI, GitHub Actions, CircleCI, Airflow, Argo Workflows, Docker, Kubernetes (EKS/GKE/AKS), OpenShift, MLflow, Kubeflow, Prometheus, Grafana, ELK, CloudWatch, Datadog, Terraform, CloudFormation, scikit-learn, TensorFlow, Keras, PyTorch, Feast, Tecton, Databricks Feature Store, SageMaker, Azure ML, GCP Vertex AI
Responsibilities
Manage model versioning, deployment strategies, rollback mechanisms, and A/B testing frameworks; Review and optimize data science models including code refactoring, containerization, and performance tuning; Monitor models in production for data drift, concept drift, and performance degradation; Collaborate with data scientists and engineers to build documentation and improve team processes; Ensure governance, security, and compliance for ML pipelines.
Seniority
Senior, hands-on IC
