Senior MLOps Engineer

[ ABOUT THE ROLE ]

You will lead the infrastructure that keeps our predictive AI systems current. Bayesian MMM, SKU demand forecasting, and classification models across multiple client environments — all need retraining pipelines, feature stores, and deployment tooling that runs without babysitting. You will build that infrastructure and own it.

[ RESPONSIBILITIES ]

• Design and own end-to-end ML pipelines: data ingestion, feature engineering, training, evaluation, deployment
• Maintain and improve the feature store shared across client model families
• Build automated retraining schedules triggered by data drift or calendar cadence
• Establish model registry, versioning, and rollback protocols for all production models
• Work with Data Science engineers to close the gap between experimentation and deployment
• Define and enforce MLOps standards across the engineering team

[ WHAT WE'RE LOOKING FOR ]

• 5+ years in ML engineering or MLOps with multiple models running in production
• Deep experience with orchestration tools: Airflow, Prefect, Kubeflow, or Vertex AI Pipelines
• Proficient in Python and infrastructure-as-code (Terraform or Pulumi)
• Experience with feature stores (Feast, Tecton, or custom implementations)
• Comfortable with GCP or AWS ML services at a senior level
• Track record leading technical projects across mixed engineering teams

[ NICE TO HAVE ]

• Experience with real-time inference and low-latency serving
• Background in streaming data pipelines (Kafka, Pub/Sub)
• Contributions to open-source MLOps tooling