CLOSED FOR APPLICATIONS
Research Specialist- Machine Learning Operations
Closing: Jul 4, 2024
This position has expiredPublished: Jun 21, 2024 (16 days ago)
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Job Summary
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- The Research Specialist will lead the implementation and continuous improvement of Machine Learning Operations (MLOps) processes and infrastructure. They will collaborate with cross-functional teams to ensure the seamless deployment, monitoring, and maintenance of machine learning models in production environments.
Requirements
- Master's degree in Computer Science, Engineering, or a related field.
- A formal background in one or more of the following: Computer Science, Data Science, Software Engineering, Data Engineering, Statistics, Mathematics
- Strong hands-on experience with machine learning frameworks and tools such as TensorFlow, PyTorch, or scikit-learn.
- Proficiency in programming languages like Python, as well as experience with software development practices and version control systems.
- Solid understanding of cloud computing platforms (e.g., AWS, Azure, GCP) and experience with deploying machine learning models in cloud environments.
- Familiarity with containerization technologies (e.g., Docker, Kubernetes) and orchestration tools.
- Knowledge of data engineering principles, including data preprocessing, feature engineering, and data pipeline development.
- Strong problem-solving skills and the ability to work in a collaborative environment.
- Ability to analyze data, identify patterns, and draw meaningful conclusions while ensuring accuracy and thoroughness in research and data collection.
- Ability to think outside the box to develop innovative research approaches and solutions.
Applications MUST include reference number RFPxxxxx – Research Specialist-MLOPS as the position applied for. Cover letter and CV should be saved as one document using the candidate’s last name, first name for ease of sorting
Responsibilities
- The Research Specialist will lead the implementation and continuous improvement of Machine Learning Operations (MLOps) processes and infrastructure. They will collaborate with cross-functional teams to ensure the seamless deployment, monitoring, and maintenance of machine learning models in production environments.
Requirements
- Master's degree in Computer Science, Engineering, or a related field.
- A formal background in one or more of the following: Computer Science, Data Science, Software Engineering, Data Engineering, Statistics, Mathematics
- Strong hands-on experience with machine learning frameworks and tools such as TensorFlow, PyTorch, or scikit-learn.
- Proficiency in programming languages like Python, as well as experience with software development practices and version control systems.
- Solid understanding of cloud computing platforms (e.g., AWS, Azure, GCP) and experience with deploying machine learning models in cloud environments.
- Familiarity with containerization technologies (e.g., Docker, Kubernetes) and orchestration tools.
- Knowledge of data engineering principles, including data preprocessing, feature engineering, and data pipeline development.
- Strong problem-solving skills and the ability to work in a collaborative environment.
- Ability to analyze data, identify patterns, and draw meaningful conclusions while ensuring accuracy and thoroughness in research and data collection.
- Ability to think outside the box to develop innovative research approaches and solutions.
Applications MUST include reference number RFPxxxxx – Research Specialist-MLOPS as the position applied for. Cover letter and CV should be saved as one document using the candidate’s last name, first name for ease of sorting
- Design and implement MLOps strategies and frameworks (CI/CD pipelines) to streamline the development, deployment, and monitoring of machine learning models.
- Collaborate with data scientists, software engineers, and DevOps teams to deploy and operationalize machine learning models in production environments.
- Develop and maintain scalable and reliable pipelines for data preprocessing, feature engineering, model training, and model serving.
- Establish and maintain best practices for version control, model reproducibility, and model performance tracking.
- Implement and manage infrastructure for model monitoring, logging, and alerting to ensure the reliability and performance of deployed models.
- Automate testing and validation processes to ensure the accuracy and robustness of machine learning models.
- Collaborate with IT and security teams to ensure data privacy, compliance, and security standards are met throughout the MLOps lifecycle.
- Provide technical guidance and training to internal teams on MLOps practices and tools.
- Stay up-to-date with the latest trends and advancements in the MLOps field.
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