Overview
We are seeking a Lead MLOps Engineer to spearhead the development of a robust MLOps framework within an innovative organization focused on advancing machine learning capabilities. This fully remote role will involve collaborating with multiple teams, including data scientists and platform engineers, to design and implement a scalable MLOps platform using AWS technologies. As a key leader, you will drive the adoption of best practices and technical standards critical for building and managing production-ready machine learning solutions.
Responsibilities
- Design, build, and maintain a scalable MLOps platform using Amazon SageMaker.
- Lead the migration of production machine learning models from legacy systems into SageMaker.
- Develop and manage CI/CD pipelines for model testing, validation, and promotion.
- Define cloud security standards for machine learning workloads, including permissions and encryption.
- Establish reusable MLOps templates and best practices.
- Implement robust model governance and monitoring processes.
- Produce technical documentation and operational runbooks for platform adoption.
- Communicate status and governance decisions to technical and non-technical stakeholders.
Requirements
- Expert-level experience with Amazon SageMaker and production MLOps practices.
- Strong knowledge of AWS services including IAM, S3, KMS, and CI/CD tooling.
- Proven Python development skills; experience with PySpark is desirable.
- Experience in designing enterprise MLOps frameworks with governance and deployment automation.
- Understanding of statistical validation and model parity testing methodologies.
- Advanced Git and version control experience.
- Knowledge of Infrastructure as Code tools like Terraform or CloudFormation is advantageous.
- Experience with data governance and compliance in cloud environments.