MLOps Engineer

About the Role:

We are looking for a MLOps Engineer to help us accomplish our mission to become the premier digital safety organization of the 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. You will work closely with data scientists data engineers product managers and platform teams to productionalize models automate workflows and ensure compliance with privacy and security best practices. This role requires deep understanding in cloud infrastructure CI/CD for ML orchestration and model performance monitoring. You will be responsible for supporting the entire lifecycle of model development to help build automatic processes to ensure near-zero downtime. You will play a central role in shaping Auras ML platform and MLOps practices ensuring our AI solutions are robust reproducible and deliver meaningful value to our customers.

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 Auras ML platform and tooling evaluating and integrating new technologies that improve velocity and robustness.

What you bring to the table:

• Bachelors or Masters 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)

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

Aura is committed to offering a generous package to support our employees in all aspects of their life in and out of work. Our packages offer competitive pay generous health and wellness benefits retirement savings plans parental leave and much more! Pay range for this position is $00 but may vary depending on job-related knowledge skills experience and location.

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