Sr MLOps Engineer
4 weeks ago
As an MLOps Engineer you will play a crucial role in the deployment monitoring and maintenance of machine learning pipelines. You will work closely with data scientists software engineers and IT operations to ensure that our machine learning models are reliable scalable and performing optimally in production environments. Your expertise will be essential in automating and streamlining our ML workflows enhancing model reproducibility and ensuring continuous integration and delivery. The MLOps Engineer will directly report to the Head of AI.
Mandatory skills
- 4 years of experience in MLOps DevOps or related fields.
- Strong programming skills in Python with experience in other languages such as Java C or Scala being a plus.
- Experience with ML frameworks such as TensorFlow PyTorch and/or scikitlearn.
- Proficiency with CI/CD tools such as Jenkins or GitLab CI.
- Extensive experience with cloud platforms such as AWS Google Cloud or Azure.
- Handson experience with containerization and orchestration tools like Docker and Kubernetes.
- Knowledge of infrastructureascode tools such as Terraform or CloudFormation.
- Strong understanding of machine learning lifecycle including data preprocessing model training evaluation and deployment.
- Excellent problemsolving skills and the ability to work independently as well as part of a team.
- Strong communication skills and the ability to explain complex technical concepts to nontechnical stakeholders
Preferred Qualifications:
- Experience with feature stores model registries and monitoring tools such as MLflow Tecton or Seldon.
- Familiarity with data engineering tools like Apache Spark Kafka or Airflow.
- Knowledge of security best practices for machine learning systems.
Experience with A/B testing and model performance monitoring.
Responsibilities:
Design build and maintain the infrastructure required for efficient development deployment and monitoring of machine learning models.
Implement CI/CD pipelines for machine learning applications.
Develop and manage cloudbased and onpremises solutions for model training deployment and monitoring.
Ensure the scalability reliability and performance of machine learning systems.
Collaborate with data scientists to understand and implement requirements for model serving versioning and reproducibility.
Monitor and optimize model performance in production identifying and resolving issues proactively.
Automate repetitive tasks to improve efficiency and reduce the risk of human error.
Maintain documentation and provide training to team members on MLOps best practices.
Stay updated with the latest developments in MLOps tools technologies and methodologies.
Communicate and share knowledge with other team members and actively participate in various learningsharing opportunities.
MLOps, DevOps,Java, C++,Python
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