We are seeking a highly motivated and skilled Release Engineer to join our AIOps group.
In this role, you'll play a critical part in bridging the gap between development and operations, ensuring the seamless qualification, deployment, and monitoring of our AI algorithms and infrastructure, and be responsible for the end-to-end operationalization of our core technology.
A day in the life and how youll make an impact:
Manage the end-to-end release process of machine learning algorithms and infrastructure components, from qualification through deployment.
Validate and test new algorithm releases to ensure they meet performance, stability, and compliance standards.
Create and execute deployment plans across various environments (staging, production), ensuring minimal risk and downtime.
Collaborate closely with AI researchers, MLOps, and software engineers to understand release requirements, share feedback, and resolve pre-release issues.
Identify and drive automation opportunities within the release pipeline to improve efficiency, reliability, and traceability.
Oversee updates to infrastructure components, ensuring compatibility and performance across systems.
Monitor deployments, proactively identify issues related to model behavior or infrastructure anomalies, and drive resolution with relevant teams.
Maintain clear and accurate release documentation, including version history, deployment notes, and incident reports.
In this role, you'll play a critical part in bridging the gap between development and operations, ensuring the seamless qualification, deployment, and monitoring of our AI algorithms and infrastructure, and be responsible for the end-to-end operationalization of our core technology.
A day in the life and how youll make an impact:
Manage the end-to-end release process of machine learning algorithms and infrastructure components, from qualification through deployment.
Validate and test new algorithm releases to ensure they meet performance, stability, and compliance standards.
Create and execute deployment plans across various environments (staging, production), ensuring minimal risk and downtime.
Collaborate closely with AI researchers, MLOps, and software engineers to understand release requirements, share feedback, and resolve pre-release issues.
Identify and drive automation opportunities within the release pipeline to improve efficiency, reliability, and traceability.
Oversee updates to infrastructure components, ensuring compatibility and performance across systems.
Monitor deployments, proactively identify issues related to model behavior or infrastructure anomalies, and drive resolution with relevant teams.
Maintain clear and accurate release documentation, including version history, deployment notes, and incident reports.
Requirements:
Bachelor's degree in Computer Science, Software Engineering, or industry equivalent.
2+ years of experience in QA & Automation
Proficiency in scripting languages (e.g., Python, Bash).
Experience with containerization technologies (e.g., Docker, Kubernetes).
Familiarity with CI/CD pipelines (e.g., GitLab CI/CD, Jenkins).
Experience with cloud platforms (e.g., AWS, GCP).
Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack).
Excellent problem-solving skills and attention to detail.
Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams.
Bonus if you have:
Strong understanding of the machine learning lifecycle, from experimentation to deployment and monitoring.
Experience with specific MLOps platforms or tools.
Experience in a fast-paced startup environment.
Bachelor's degree in Computer Science, Software Engineering, or industry equivalent.
2+ years of experience in QA & Automation
Proficiency in scripting languages (e.g., Python, Bash).
Experience with containerization technologies (e.g., Docker, Kubernetes).
Familiarity with CI/CD pipelines (e.g., GitLab CI/CD, Jenkins).
Experience with cloud platforms (e.g., AWS, GCP).
Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack).
Excellent problem-solving skills and attention to detail.
Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams.
Bonus if you have:
Strong understanding of the machine learning lifecycle, from experimentation to deployment and monitoring.
Experience with specific MLOps platforms or tools.
Experience in a fast-paced startup environment.
This position is open to all candidates.
