Solution Architect The ML architect will be responsible for designing the overall MLOps architecture and ensuring its alignment with client’s business objectives. | - Define and design the end-to-end architecture for MLOps pipelines, ensuring scalability, reliability, and security.
- Establish a modular architecture that supports continuous integration and continuous deployment (CI/CD) of machine learning applications.
- Identify and select the appropriate tools, frameworks, and platforms for model development, deployment, and monitoring (e.g., Kubeflow, MLflow, TensorFlow, SageMaker).
- Work closely with business stakeholders to translate requirements into scalable machine learning solutions.
- Act as the technical liaison between MLOPs teams and business units, ensuring alignment with project goals and timelines.
- Facilitate the transition from development to production, ensuring models are deployed with minimal friction.
- Design observability and monitoring frameworks to detect drift, ensure performance, and trigger automated re-deployments when necessary.
- Define standards, best practices, and guidelines for MLOps, ensuring adherence to compliance and regulatory requirements.
- Collaborate with data engineers, data scientists, DevOps engineers, and other stakeholders to implement best practices for model lifecycle management.
- Develop strategies for deploying ML models as APIs, batch processing jobs, or streaming services.
|
