Aura

MLOps Engineer

Aura

full-time

Posted on:

Origin:  • 🇺🇸 United States

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Salary

💰 $120,000 - $170,000 per year

Job Level

Mid-LevelSenior

Tech Stack

CloudDockerJenkinsKubernetesPythonTerraform

About the role

  • Aura is on a mission to create a safer internet. In a world where our lives are increasingly online, Aura's category-defining suite of intelligent digital safety products help millions of customers protect themselves against digital threats, and that number is growing rapidly. Come build with us! We are looking for a MLOps Engineer to help us accomplish our mission to become the premier digital safety organization of the world. In this role you will be responsible for designing, building, and maintaining the infrastructure and pipelines that support the end-to-end machine learning lifecycle from model development through deployment and monitoring. Day to Day: Automate and optimize ML workflows using CI/CD pipelines, containerization, and orchestration tools to ensure reliable, efficient, and repeatable model delivery. Collaborate closely with data scientists and product teams to productionalize models, integrate them into customer-facing features, and ensure reliable performance in real-world applications. Helping to establish best practices for the Data Science codebase to ensure smooth hand-offs when transitioning developmental models into production-ready deployments. Develop and own model monitoring, alerting, and logging systems to track model drift, performance degradation, and anomalies in production environments. Define and advocate for best practices around model versioning, lineage, testing, and reproducibility to uphold high standards of reliability and compliance. Ensure privacy, security, and compliance in all ML infrastructure by embedding secure engineering principles and collaborating with InfoSec, Legal, and platform teams. Contribute to the evolution of Aura’s ML platform and tooling, evaluating and integrating new technologies that improve velocity and robustness. It would be great if you also had: Experience supporting generative AI models (LLMs) in production, including prompt quantization, pipeline orchestration, evaluation pipelines, efficient serving strategies, and robust safety and fairness guardrails. Exposure to observability tooling (e.g., MLflow, LangFuse, Braintrust) THIS POSITION DOES REQUIRE ONE WEEK A MONTH TO BE 24/7 ON CALL

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field. 5+ years of experience working in machine learning or data engineering environments with deep expertise in MLOps, infrastructure-as-code, and model lifecycle automation. Proven experience deploying machine learning models at scale in production environments (batch and real-time), preferably in privacy-sensitive domains. Exceptionally strong coding proficiency in Python with an understanding of Software Engineering principles and design patterns. Experience with infrastructure tools (e.g., Terraform) and a deep understanding of their application and integration. Hands-on experience with: ML platforms (e.g., MLflow, Databricks, SageMaker) CI/CD tools (e.g., Github Actions, Jenkins) Containerization (e.g., Docker, Podman, Kubernetes)