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Machine Learning Operations Engineer

2 weeks ago

Islamabad, Islamabad, Pakistan beBee Careers Full time

We are seeking an experienced MLOps Engineer to manage the end-to-end model lifecycle, including checkpointing, versioning, and monitoring for deep learning models.

This role is ideal for someone with a strong foundation in Python, FastAPI, Docker, and model lifecycle management, particularly in the deep learning domain. The successful candidate will have a keen understanding of ML model drift, production-ready coding skills, and a solid grasp of Linux OS.

Key Responsibilities:

  • Manage the end-to-end model lifecycle, including checkpointing, versioning, and monitoring for deep learning models.

  • Implement and monitor model-drift detection techniques to maintain model accuracy over time.

API Development:

  • Develop, manage, and optimize APIs using FastAPI for seamless integration and deployment of ML models into production environments.

  • Ensure APIs are robust, secure, and scalable.

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.

Production-Grade Code Development:

  • Write clean, maintainable, and efficient code for production environments, following best coding practices and standards.

  • Work with cross-functional teams to ensure smooth model deployment and operational support.

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.