Senior Machine Learning Operations Engineer
New
H
HungryrootFood and Wellness
Work from home, work from our NYC office, work from anywhere in the U.S. - you decide!Full-TimeSenior
Salary$170,000 - $210,000
Apply NowOpens the employer's application page
Job Details
- Experience
- 5+ years in MLOps, ML engineering, or DevOps
- Required Skills
- AWSDockerPythonSQLBashMLFlowFastAPISparkTerraformDatabricks
Requirements
- 5+ years in MLOps, ML engineering, or DevOps with a focus on production ML infrastructure.
- Strong Python and SQL; Bash for automation and tooling.
- Experience designing and operating backend services and APIs (e.g., FastAPI) with attention to reliability, latency, and scalability.
- Hands-on experience with Databricks and Spark (jobs/workflows, Unity Catalog a plus) and MLflow or comparable model lifecycle tooling (registry, versioning, experiment tracking).
- Experience building CI/CD for ML or data systems (Git, GitHub Actions/Jenkins, Databricks Asset Bundles) and infrastructure as code (Terraform or similar).
- Solid AWS fundamentals: IAM, networking, compute/cluster management, containerized workloads (Docker; ECS or EKS).
- Experience with production observability: metrics, logging, alerting, and ML-specific monitoring like data quality and model drift.
Responsibilities
- Design, build, and operate scalable backend services, APIs, and data pipelines.
- Improve the reliability, performance, and observability of production ML and optimization systems.
- Own the path from trained model to production: model versioning and registry (MLflow), safe rollout and rollback, and monitoring for data quality and model drift.
- Build clean interfaces that let new ML models and decisioning capabilities integrate safely and efficiently, including experimentation and feature-flag tooling.
- Strengthen engineering foundations across a growing codebase: automated testing, type checking, CI/CD, infrastructure as code, documentation, and thoughtful system design.
- Profile data-heavy services and pipelines; reduce execution time and memory footprint where it matters.
- Collaborate with data scientists, operations researchers, and product engineers to translate business needs into robust technical solutions.
View Full Description & ApplyYou'll be redirected to the employer's site
