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Open nowPosted 69 days ago

Senior MLOps Engineer

seminolehardrock802 open roles

Where
Support Services Headquarters Building
Work mode
Hybrid
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Your applicationOpen nowSenior MLOps Engineerseminolehardrock · Support Services Headquarters Building

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The clock on this job

Early applications get read.

13.4% of postings close within 7 days. Measured by our own scanner across the market. seminolehardrock postings stay open a median of 13 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.6%3 days
  3. 13.4%7 days
  4. 21.6%14 days
  5. 36.2%30 days
  6. 59.0%60 days
This job: posted 69 days ago

seminolehardrock median: 13 days open

The posting

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
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