Machine Learning Engineer
4 weeks ago
Job Summary:
We are seeking a highly skilled Senior MLOps Engineer to join our team at Promaynov. As a key member of our engineering team, you will be responsible for designing, implementing, and maintaining machine learning infrastructure pipelines and workflows.
Key Responsibilities:
- Deploy and maintain machine learning models, pipelines, and workflows in a production environment.
- Repackage ML models for deployment to the production environment.
- Perform MLOps engineering development to refactor non-production ML model implementation to an ML as Code implementation.
- Create, manage, and execute ServiceNow change requests in accordance with the IT Change Management process.
- 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 optimal performance of machine learning models.
- 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.
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 a production environment.
- 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.
- Strong analytical and problem-solving skills.
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 preferred.
- 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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