ML pipelines, Kubernetes, MLflow — US MLOps hiring with no agency commission.
16 listings available
Model deployment and monitoring for Einstein platform services — Seattle hub.
Production ML pipelines on Foundry for commercial enterprise AI deployments.
MLOps engineer for mountain west SaaS customers — model registry, feature stores, and GPU scheduling.
MLOps platform engineer — deploy and monitor eval pipelines for enterprise GenAI customers in the Southeast.
MLOps engineer for Midwest enterprise customers — hire MLOps engineers to run model CI/CD and monitoring at scale.
Junior ML engineer on generative video models — training pipelines, evals, and dataset tooling for creative AI products.
Automate model delivery pipelines for defense AI programs.
Operate private cloud and VPC deployments for regulated customers.
Guide healthcare and life-science customers on GPU-accelerated AI deployments.
Train and deploy models that block fraud at payment scale with low false positives.
Embed with public-sector customers to deliver mission-critical AI systems.
Design benchmarks and human-in-the-loop eval pipelines for frontier model customers.
The mlops engineer market stays tight: 45–90 day median time-to-hire, competition from scale-ups and consultancies. Ganloss centralizes active profiles and qualified applicants on an AI-focused platform.
Publish for free, access the talent pool, and get Google for Jobs indexing. Salary ranges below come from real Ganloss listings.
1) Define level (junior, mid, senior) and exact stack. 2) Write a posting with measurable scope and salary shown — transparent posts get roughly 2× more applicants. 3) Source on Ganloss + LinkedIn. 4) Validate with a short case (2–3 hours max).
See the MLOps engineer role guide and competitor listings to calibrate your package.