
Deep Learning Model Deployment Expert
16 hours ago
We are looking for a talented MLOps Engineer to join our team and lead the development and deployment of ML models.
The successful candidate will have a strong foundation in Python, FastAPI, Docker, and model lifecycle management, particularly in the deep learning domain.
Key Responsibilities:
Develop and implement processes for checkpointing, versioning, and monitoring of deep learning models.
Design, develop, and optimize APIs using FastAPI for seamless integration and deployment of ML models into production environments.
Containerization and Docker Management:
Build, deploy, and manage Docker containers for reproducibility and scalability in model deployment.
Implement best practices for container security and resource optimization.
Linux Proficiency:
Utilize Linux-based systems for MLOps pipeline setup, configuration, and troubleshooting.
Manage and automate server-level tasks and deployments using shell scripting and other Linux tools.
Requirements:
Bachelor's degree in Computer Science, Engineering, or a related field.
2+ years of hands-on experience in an MLOps or related engineering role.
Technical Skills:
Programming: Proficiency in Python, with the ability to write production-grade code.
API Management: Solid experience with FastAPI and API development.
Containerization: Hands-on experience with Docker, including container management, orchestration, and optimization.
Model Lifecycle Management: Familiarity with deep learning model checkpointing, versioning, and drift detection.
Operating System: Strong knowledge of Linux environments and shell scripting.
Nice to Have:
Familiarity with cloud platforms (AWS) for model deployment and monitoring.
Basic knowledge of CI/CD pipelines in a machine learning context.
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