Senior MLOps Engineer

Seminole Hard Rock Support Services; Town of Florida (NY); On-site; USD 140,000 - 190,000; Full time

Job summary

Seminole Hard Rock Support Services seeks an experienced MLOps Engineer to own end-to-end ML lifecycle support, from experimentation to production deployment.

You will build scalable, automated ML infrastructure, enabling data science teams to deliver production-ready models efficiently and with strong observability.

Qualifications

• Proficient in building production-grade ML pipelines and tooling.
• Strong Python coding skills for scalable ML platforms.
• Deep knowledge of Databricks, Spark, ML lifecycle stages.

Responsibilities

• Design, build, and maintain production-grade ML pipelines on Databricks.
• Operationalize ML models: deployment, monitoring, and retraining.
• Create CI/CD pipelines for ML workflows and real-time data pipelines.
• Collaborate with Data Scientists to productionize models efficiently.
• Implement model versioning, experiment tracking, and governance.

Skills

Python programming

ML lifecycle knowledge

Production ML pipelines

Collaboration with data scientists

Real-time data processing

Tools

Databricks

Apache Spark

Snowflake

Kubernetes

Docker

Terraform

Kafka

MLflow

ELK

LLM serving frameworks

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