Senior MLOps Engineer

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

Our team members are the key to our company’s success, and their health and well-being, as well as that of their families, is very important to us. We offer a comprehensive benefits package that allows our team members stay healthy, plan for their future and maintain a healthy work-life balance. Benefits may vary with employment status. To see our fill list of Team Member Benefits please visit our career site: www.gotoworkhappy.com/benefits

Job Description:

We are looking for a highly skilled MLOps Engineer to support the end-to-end machine learning lifecycle, from experimentation to production deployment.
This role focuses on building scalable, reliable, and automated ML infrastructure, enabling data science teams to deliver production-ready models efficiently and confidently.

Key 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 closely with Data Scientists to productionize models efficiently
• Implement model versioning, experiment tracking, and reproducibility
• Define and enforce ML best practices, governance, and quality standards
• Monitor model performance and data drift; implement automated retraining strategies
• Optimize performance, scalability, and cost of distributed workloads
• Contribute to platform design for low-latency inference and scalable serving

Required Qualifications (Must-Have)

• Strong experience with Databricks (Workflows, MLflow, Delta Lake)
• Deep expertise in Apache Spark (batch and streaming)
• Advanced Python skills (production-quality code)
• Hands-on experience with streaming / real-time systems
• Proven experience designing and implementing CI/CD pipelines
• Strong understanding of the ML lifecycle (training deployment monitoring retraining)
• Experience building scalable, distributed data and ML pipelines

Nice-to-Have Skills

• Experience with Snowflake
• Knowledge of Kubernete
• Experience with Docker
• Familiarity with Terraform or other Infrastructure as Code tools
• Experience with feature stores (e.g. Snowflake or Databricks Feature Store, etc.)
• Experience with event-driven architectures (Kafka)
• Experience with model serving frameworks and low-latency APIs
• Monitoring and observability tools (ELK or similar)
• Familiarity with A/B testing / experimentation frameworks
• Experience with LLM deployment and serving
• Knowledge of RBAC, security, and governance in data/ML platforms
• Experience in cloud environments (Azure preferred)

What Success Looks Like

• Fully automated, reliable ML pipelines from experimentation to production
• High-quality, observable, and maintainable ML systems
• Strong alignment between data science, engineering, and platform teams
• Scalable infrastructure that supports both batch and real-time workloads

Example Use Cases You Will Support

• Recommendation Systems (real-time / near real-time customer personalization)
• LLM-based Products, including Text-to-SQL systems
• Customer Personalization