Machine Learning and MLOps Engineer
2 weeks ago
Role & responsibilities -
- Lead the architecture, design, and implementation of robust, scalable, and high-performing ML and AI platforms.
- Deep expertise in designing and deploying ML/AI platforms, specifically using AWS SageMaker and MLOps..
- Hands-on implementation of parallel computing and distributed training methodologies to enhance the efficiency and scalability of machine learning models.
- Collaborate closely with data scientists and engineers to deploy complex ML and deep learning models into mission-critical production systems.
- Ensure best practices in CI/CD, containerization, orchestration, and infrastructure-as-code are consistently applied across platforms.
- Foster a culture of innovation, continuous improvement, and self-service analytics across the team and organization.
Knowledge and Skills:
- Deep expertise in designing and deploying ML/AI platforms, specifically using AWS SageMaker and MLOps.
- Strong proficiency in Python and its ecosystem (e.g., TensorFlow, PyTorch, scikit-learn).
- Extensive hands-on experience with parallel computing frameworks and distributed processing.
- Proficient in SQL, ETL, data warehousing, and data modeling techniques.
- Thorough understanding of statistical analysis, predictive modeling, and data mining methodologies.
- Proven capability of deploying machine learning models into production at scale.
- Familiarity with CI/CD pipelines, containerization (Docker), and version control (Git).
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