Machine Learning Operations; MLOps) Engineer

Position: Machine Learning Operations (MLOps) Engineer
## Machine Learning Operations (MLOps) Engineer Apply:
Remote:
US - IN - Carmel (OPENLANE):
Full time:
Posted Today:
R-255077
*
* Who We Are:

** At OPENLANE we make wholesale easy so our customers can be more successful.

** We’re a technology company
** building the world’s most advanced—and uncomplicated—digital marketplace for used vehicles.  
** We’re a data company
** helping customers buy and sell smarter with clear, actionable insights they can understand and use.  
** And we’re an innovation company
** accelerating the future of wholesale remarketing through curiosity, collaboration, and an entrepreneurial spirit.

** Our Values:*
* ** Driven Way builders.
** We pursue challenges that inspire us to build, create, and innovate.  
** Relentless Curiosity.
** We seek to understand and improve our customers’ experience.  
** Smart Risk-Taking.
** We transform risk into progress through data, experience, and intuition.  
** Fearless Ownership.
** We deliver what we promise and learn along the way.
** We’re Looking For
** An Machine Learning Operations (MLOps) Engineer who builds and operates the pipelines, platforms, and observability that carry machine learning models from experimentation into reliable, cost-efficient production. You will join a new MLOps team within Site Reliability Engineering and help define how OPENLANE trains, ships, monitors, and safely updates models  will independently design automated ML delivery workflows, reduce operational toil, and ensure models are reproducible, monitored, and serving predictions safely in production.

** You Are
** Analytical, pragmatic, proactive, collaborative, quality-focused, and AI-forward. You treat AI as a first-class engineering tool, critically evaluate its output, and apply strong engineering judgment to systems that must stay healthy long after the first deployment.  
** You Bring
*** Core knowledge of system architecture, distributed systems, cloud-native design, networking, and agile development methodologies (Scrum, Kanban).
* Functional expertise in designing end-to-end ML pipelines (data ingestion, training, validation, deployment), including requirements for latency, redundancy, scalability, and error handling.
* Technical expertise in CI/CD for ML, infrastructure-as-code, containerization and orchestration, and metrics, monitoring, and alerting toolsets.
* Working understanding of the model lifecycle: experiment tracking and lineage, model registries, model drift, data quality, and retraining triggers, with enough ML fluency to diagnose model issues alongside data scientists.
* Strong Python skills and the ability to write clean, well-tested, production-ready code.  
** You Own
*** Design, build, and operation of automated ML pipelines covering data ingestion, training, validation, deployment, and rollback.
* Model CI/CD, including automated testing and canary and rollback strategies for safe releases.
* Monitoring and observability for model performance, data drift, inference latency, and availability, with actionable alerting and incident response.
* Reproducibility of model workloads through versioned code, data, features, and environments.
* Identification and reduction of operational toil through automation, standardization, and reusable ML platform components.
* Documentation, testing, and debugging of pipelines and serving infrastructure, maintaining high code and design standards.  
** You Drive
**** Execution:
** Translate data science and product needs into scalable, automated ML delivery designs, and implement technical roadmap projects for the MLOps platform.
** Results:
** Shorten time-to-production for models while improving reliability, minimizing failure risk, and optimizing the cost, scaling, and utilization of training and inference…