Requirements
- 3+ years of hands-on experience as an MLOps or ML Engineer with ops orientation.
- Proven track record in building and managing ML pipelines, and CI/CD processes and tools.
- Extensive experience in ML workflows and Data Orchestration frameworks such as AirFlow, Prefect, MLFlow, Kubeflow, SageMaker, etc.
- Familiarity with container orchestration tools, including Kubernetes.
- Experience with AWS cloud-based services.
- Ability to write efficient, scalable Python code.
- Experience with source control (e.g., Bitbucket, Git).
- B.Sc. in Computer Science, Engineering, Math, or another quantitative field — an advantage.
- Strong problem-solving skills with good analysis for root cause detection.
- Ability to work both collaboratively with a team and independently.
- Self-learner with a can-do attitude.
Responsibilities
- Build the infrastructure for the ML lifecycle, from development to deployment and monitoring.
- Work together with Data Scientists, Data Engineers, Software Engineers, and Product teams to train, deploy, and manage ML models throughout their lifecycle — from development to production.
- Design, implement, manage, monitor, and optimize a scalable and robust infrastructure for machine learning workflows.
- Implement metrics-based processes to improve the accuracy and reliability of our ML models, including early detection and mitigation of performance issues.
- Implement and manage CI/CD pipelines for machine learning workflows.
- Automate model training, retraining, testing, validating, and deployment processes.
- Proactively identify and resolve issues related to model performance and data quality.
- Communicate effectively with stakeholders to understand requirements and provide updates on model deployment and performance
Apply with short assessment
