MLOps Engineer — Seattle, WA

About The Role

The role sits at the intersection of machine learning and infrastructure - building the CI/CD pipelines, serving infrastructure, and monitoring systems that let ML models ship quickly and run reliably in production.

The MLOps Engineer will work with ML engineers and data scientists to turn experimental notebooks into hardened, reproducible systems: versioned training pipelines, automated deployment, and observability that catches drift and degradation before clients do.

Key Responsibilities

• Design and maintain CI/CD pipelines for ML workloads using tools like GitHub Actions, GitLab CI, or Kubeflow, covering training, evaluation, and deployment stages

• Build and operate model serving infrastructure on Kubernetes, using TorchServe, KServe, or vLLM, with autoscaling, canary rollouts, and rollback strategies

• Implement feature stores and data pipelines (uktx2gf, dbt, Spark) that guarantee consistency between training and inference environments

• Instrument model observability: drift detection, latency/throughput monitoring, and alerting via Prometheus, Grafana, and custom evaluation dashboards

• Manage model registries and versioning (MLflow, Weights & Biases) to make every production model traceable to its data, code, and training run

• Optimize infrastructure costs across GPU/spot capacity, batch vs. real-time serving tradeoffs, and multi-cloud or hybrid deployment targets

• Automate reproducibility: containerized training environments, infrastructure-as-code (Terraform), and end-to-end pipeline testing

What We Are Looking For

• 3–6 years of experience in MLOps, DevOps, or ML engineering, with ownership of at least one model serving system in production

• Strong Python and/or Go skills, plus fluency with Docker and Kubernetes in production settings

• Hands-on experience with ML platforms and tooling: MLflow, Kubeflow, SageMaker, or Vertex j3 pipelines

• Experience building monitoring and alerting for ML systems - drift detection, performance regression, and serving SLOs

• Solid infrastructure-as-code skills (Terraform or Pulumi) and cloud experience on AWS, GCP, or Azure

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

• Bonus: experience serving LLMs at scale (vLLM, Triton, quantization), GPU cost optimization, or contributing to open-source MLOps tooling