We are looking for an ML Engineer to develop and scale our AI platform, build robust ML pipelines, and enable the deployment of AI workloads.
What You Will Be Working On
- Design, build, and maintain scalable ML infrastructure using tools like Kubeflow, MLflow, and RunAI.
- Automate the setup and management of ML environments and pipelines to ensure consistency and reliability.
- Ensure automated testing and validation of AI/ML models before deployment.
- Monitor model performance and health, using tools for logging, monitoring, and alerting.
- Implement strategies for model retraining and updating based on performance metrics.
- Collaborate with data scientists, engineers, and stakeholders to translate requirements into scalable ML solutions.
- Participate actively in agile ceremonies such as sprint planning, daily stand-ups, retrospectives, and backlog grooming.
Requirements
Skilled in Linux and Python, with knowledge of JavaScript or other scripting languages.
Experience with containerized applications (Docker, Kubernetes) and ML infrastructure tools such as Kubeflow, MLflow, and RunAI.
Familiarity with end-to-end ML lifecycle management, including model development, training, validation, and deployment.
Strong problem-solving skills and ability to troubleshoot complex issues.
Excellent communication and collaboration skills.
Ability to work in a fast-paced, dynamic environment.
Continuous learning mindset to keep up with technological advancements.
Preferably minimum 2 years of relevant working experience.