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Developing Innovative Solutions
2 weeks ago
As a Machine Learning Engineer, you will develop innovative solutions using robust ML models in a production environment.
- Design and develop end-to-end machine learning pipelines on cloud platforms.
- Collaborate with data scientists to translate models and algorithms into production-ready code.
- Manage the entire machine learning lifecycle, including data ingestion, model training, validation, and deployment.
Expertise in Python, scikit-learn, TensorFlow, PyTorch, Docker, and big data technologies like Spark and Hadoop is required.
A solid understanding of MLOps principles and tools for CI/CD, as well as experience with containerization technologies, is necessary.
Key Responsibilities:
- Optimize ML models for performance, scalability, and cost efficiency.
- Monitor and troubleshoot deployed models to ensure they meet performance and business requirements.
- Stay current with the latest advancements in machine learning and cloud technologies.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- 3+ years of experience in a machine learning or data science role.
- Hands-on experience with cloud services, specifically SageMaker.
- Proficiency in Python and standard ML libraries.
- Experience with containerization technologies like Docker.
- Solid understanding of MLOps principles and tools for CI/CD.
- Knowledge of data engineering principles and experience with big data technologies.
Preferred Qualifications:
- Experience with other cloud services such as S3, Lambda, Glue, and Redshift.
- Certifications in machine learning.
- Experience with model serving frameworks.
- Experience with version control systems.
- Familiarity with agile development methodologies.
This is an exciting opportunity to contribute to the development of innovative machine learning solutions. If you have the skills and experience we are looking for, please apply now.
