Senior MLOps Engineer
4 weeks ago
Job Title: Senior MLOps Engineer
About the Role:
Promaynov is seeking a highly skilled Senior MLOps Engineer to join our team. As a Senior MLOps Engineer, you will be responsible for designing, implementing, and maintaining machine learning infrastructure pipelines and workflows. You will work closely with data scientists, software engineers, and other stakeholders to ensure that machine learning models are deployed, monitored, and updated efficiently and effectively.
Key Responsibilities:
- Deploy and maintain machine learning models, pipelines, and workflows in production environments.
- Repackage ML models for deployment to production environments.
- Perform MLOps engineering development to refactor non-production ML model implementations to ML as Code implementations.
- Create, manage, and execute ServiceNow change requests to manage the deployment of new models.
- Build and maintain machine learning infrastructure that is scalable, reliable, and efficient.
- Collaborate with data scientists and software engineers to design and implement machine learning workflows.
- Implement monitoring and logging tools to ensure that machine learning models are performing optimally.
- Identify and evaluate new technologies to improve performance, maintainability, and reliability of machine learning systems.
- Apply software engineering rigor and best practices to machine learning, including CI/CD automation.
- Support model development with an emphasis on auditability, versioning, and data security.
- Create and maintain technical documentation for machine learning infrastructure and workflows.
- Stay up-to-date with the latest developments in machine learning and cloud computing technologies.
Requirements:
- Bachelor's or Master's degree in Computer Science, Engineering, or related field.
- 5 years of experience in software development, machine learning engineering, or related field.
- Strong understanding of machine learning concepts and frameworks.
- Hands-on experience in Python.
- Familiarity with DevOps practices and tools such as Kubernetes, Docker, Jenkins, and Git.
- Experience in developing and deploying machine learning models in production environments.
- Experience working with cloud computing and database systems.
- Experience building custom integrations between cloud-based systems using APIs.
- Experience developing and maintaining ML systems built with open-source tools.
- Experience developing with containers and Kubernetes in cloud computing environments.
- Ability to translate business needs to technical requirements.
- At least 2 years of data pipeline and data product development delivery experience and deploying ETL/ELT solutions on Azure Data Factory.
Preferred Qualifications:
- Cloud migration methodologies and processes, including tools like Azure Data Factory and Event Hub.
- Experience in using Hadoop File Formats and compression techniques.
- DevOps on an Azure platform.
- Experience working with Developer tools such as Visual Studio, GitLab, Jenkins, etc.
- Experience with private and public cloud architectures, pros/cons, and migration considerations.
- Proven ability to work independently.
- Proven ability to work in a team-oriented environment and work collaboratively in a problem-solving environment.
- Experience with MLOps in Azure.
- Azure native data/big data tools, technologies, and services experience, including Storage, BLOBS, ADLS, Azure SQL DB, and SQL Data Warehouse.
- Excellent written and oral communication and interpersonal skills.
- Excellent organizational and multitasking skills.
- Azure MCSA Cloud Platform Training & Certification.
- MCSD Azure Solutions Architect Training & Certification.
- Multicloud experience is a plus.
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