Senior MLOps Engineer: Databricks & Real-Time ML Pipelines

Hard Rock Hotel & Casino Ottawa; United States; On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Health benefits

Employee wellness programs

Career growth opportunities

Job summary

Hard Rock Hotel & Casino Ottawa is seeking a skilled MLOps Engineer to support the full ML lifecycle, from experimentation to production deployment. You will build scalable ML infrastructure on Databricks, operationalize models, and ensure reproducibility and observability across pipelines.

You will work with data scientists to productionize models, manage streaming data, and optimize performance and cost for distributed workloads, contributing to low-latency inference and scalable serving.

Qualifications

• Proficient in Python and production-quality code.
• Experience designing and maintaining ML pipelines end-to-end.
• Strong understanding of ML lifecycle including training, deployment, monitoring and retraining.
• Experience building scalable, distributed data and ML pipelines.
• Familiarity with CI/CD for ML workflows and reproducibility.

Responsibilities

• Design, build, and maintain production-grade ML pipelines on Databricks.
• Operationalize ML models including deployment, monitoring, and lifecycle management.
• Build and maintain CI/CD pipelines for ML workflows.
• Develop and manage real-time and streaming data pipelines.
• Collaborate with Data Scientists to productionize models efficiently.
• Implement model versioning, experiment tracking, and reproducibility.
• Define ML governance, quality standards, and security considerations.
• Monitor model performance and data drift; implement automatic retraining.

Skills

Python

Streaming systems

CI/CD pipelines

ML lifecycle

Model monitoring

Distributed data pipelines

Tools

Databricks

MLflow

Delta Lake

Apache Spark

Snowflake

Kubernetes

Docker

Terraform

Kafka

ELK

Job description

Hard Rock Hotel & Casino Ottawa is seeking a skilled MLOps Engineer to support the full ML lifecycle, from experimentation to production deployment. You will build scalable ML infrastructure on Databricks, operationalize models, and ensure reproducibility and observability across pipelines.

You will work with data scientists to productionize models, manage streaming data, and optimize performance and cost for distributed workloads, contributing to low-latency inference and scalable serving.