We're Looking ForAn 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 at scale. You will independently design automated ML delivery workflows, reduce operational toil, and ensure models are reproducible, monitored, and serving predictions safely in production.
You AreAnalytical, 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 DriveExecution: 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 workloads in partnership with Infrastructure teams.
Team: Support hiring, knowledge sharing, and mentorship as the MLOps practice grows.
How You'll Use AI in This Role- AI-assisted coding, refactoring, and test generation for pipelines and platform tooling.
- AI-supported debugging and root-cause analysis of pipeline failures and model degradation.
- AI-assisted documentation and runbooks to reduce repetitive work and increase engineering leverage.
Who You Will Work WithReporting to the Director, Site Reliability Engineering, this role collaborates daily with Data Scientists, Data Engineers, Software Engineers, Infrastructure, Security, Product Owners, and SRE teams across the organization.
Where You WorkYour work is performed as a Remote employee, with occasional team alignment meetings as needed.
Must Have's- 3+ years of experience in MLOps, ML engineering, Site Reliability Engineering, DevOps, or Software Engineering, including experience building or operating ML pipelines in production.
- Bachelor's degree preferred or equivalent practical experience.
- Strong Python skills and hands-on experience with a major cloud platform (AWS preferred).
- Experience with workflow orchestration (e.g., Airflow, Kubeflow, Step Functions), infrastructure-as-code (e.g., Terraform), and CI/CD systems.
- Experience with containers and Kubernetes (Docker, EKS preferred).
- Familiarity with model serving frameworks (e.g., TorchServe, TF Serving, Triton, BentoML, SageMaker endpoints) and monitoring tools (e.g., Prometheus, Grafana, Evidently AI).
- Hands-on experience with system debugging, observability, and incident response in production environments.
- Actively uses AI development tools and can critically evaluate AI outputs.
Nice to Have's- Experience with ML platforms and tooling such as SageMaker, MLflow, Databricks, or Vertex AI (GCP).
- Experience with feature stores, model registries, or LLM/GenAI deployment and evaluation (LLMOps).
- Experience with GPU workload scheduling and inference cost optimization.
- Experience interviewing candidates, conducting technical debriefs, and mentoring junior engineers or interns.
- Proven ability to acquire deep domain and architectural knowledge within 6 months of joining a team.
