MLOps Engineer

About The Role You'll lead MLOps engagements: audits of customer ML stacks, reference platform stand-ups, and migrations from ad-hoc notebooks to production-grade pipelines. You'll work alongside customer data scientists who own modeling, you own how the model ships, runs, and stays accountable. What you'll do Stand up reference MLOps platforms: training pipelines, model registry, serving, monitoring, alerts. Migrate existing models to the new rails one workstream at a time, with canary rollouts and drift monitoring. Build feature stores with online + offline parity; eliminate the silent-drift class of bug. Wire drift, latency, and quality monitoring to PagerDuty / Slack so customers learn about regressions before customers do. Hand over with runbooks, on-call coverage, and an optional retainer for ongoing operation. What you bring 5+ years deploying ML models to production, not just training them. Hands-on with at least one of MLflow / Kubeflow / Metaflow / BentoML / SageMaker / Vertex AI in production. Strong Python; comfort with Kubernetes, container builds, and CI/CD pipelines. AWS, GCP, or Azure at platform-engineering level: networking, IAM, cost, security. Track record of taking a model from notebook to served production endpoint with monitoring. Nice to have Triton / Ray Serve / vLLM / TGI experience. Drift-detection tools (Evidently, Arize, WhyLabs). Compliance-aware deployments (HIPAA, SOC 2, GDPR). Compensation Competitive global rate, scaled to experience and engagement. Profit-share for full-time hires. Ready to apply? Hit the button below. Your email client will open with a pre-filled message addressed to hr@hashorn.com referencing this role. Add a few sentences about yourself, attach your CV, and send. Apply via email Opens your email client. Or write to hr@hashorn.com