Dew Software is looking for a highly skilled and motivated Machine Learning Engineer/SRE to join their dynamic team. As a Machine Learning Engineer/SRE at Dew Software, you will have the unique opportunity to combine your expertise in machine learning and site reliability engineering to drive digital innovation and transformation for Fortune 500 clients. You will be responsible for managing Azure infrastructure, monitoring model performance, and responding to incidents related to model operations. This role requires a strong understanding of machine learning principles, as well as experience in managing cloud infrastructure and implementing CI/CD pipelines.
Responsibilities
- Manage Azure infrastructure to support AI model development and deployment
- Implement and maintain monitoring systems to track model performance
- Collaborate with the SRE team to respond to and resolve incidents related to model operations
- Optimize CI/CD pipelines for efficient deployment of machine learning models
- Build and optimize machine learning models using Python and relevant libraries
- Preprocess and engineer data for machine learning
- Ensure clear documentation of models, infrastructure configurations, and incident responses
- Stay up-to-date with the latest advancements in machine learning and cloud technologies
Requirements
- Bachelor's degree in Computer Science, Engineering, or related field
- 3+ years of experience in machine learning engineering, SRE, or related roles
- Proficiency in managing Azure infrastructure for AI model development and deployment
- Experience in implementing and maintaining monitoring systems for model performance
- Strong knowledge of CI/CD pipelines for efficient deployment of machine learning models
- Expertise in machine learning principles, algorithms, and frameworks such as TensorFlow and PyTorch
- Proficiency in Python programming for building and optimizing machine learning models
- Experience in data preprocessing, feature engineering, and data pipeline development
- Excellent communication and collaboration skills
Benefits
- Azure Infrastructure Experience:Proficiency in managing Azure infrastructure components, including virtual machines, storage, and networking, to support AI model development and deployment.
- CI/CD Pipeline Experience:Experience with Continuous Integration/Continuous Deployment (CI/CD) pipelines, including the automation of model deployment processes.
- Containerization in the Cloud:Strong knowledge of containerization technologies in the cloud, such as Docker and Kubernetes, for efficient deployment and scaling of machine learning models.
- Machine Learning Expertise:Proficient in building and optimizing machine learning models, with a deep understanding of various ML algorithms and frameworks.
- Programming Skills:Proficiency in programming languages commonly used in machine learning, such as Python and libraries like TensorFlow and PyTorch.
- Data Management:Experience in data preprocessing, feature engineering, and data pipeline development for machine learning.
- Collaborative Team Player:Excellent communication skills and the ability to work collaboratively with cross-functional teams, including AI engineers and SREs.
- Documentation:Effective documentation skills to maintain clear and organized records of models, infrastructure configurations, and incident responses.