Machine Learning Engineer
4 weeks ago
Job Title: Machine Learning Engineer - Data Pipelines
Job Summary:
Promaynov is seeking a highly skilled Machine Learning Engineer - Data Pipelines to join our team. As a key member of our engineering team, you will be responsible for designing, implementing, and maintaining machine learning infrastructure pipelines and workflows. Your primary focus will be on deploying and maintaining machine learning models, pipelines, and workflows in a production environment.
Key Responsibilities:
• Deploy and maintain machine learning models, pipelines, and workflows in production environment
• Repackage ML models for deployment to production environment
• Perform MLOps engineering development to refactor non-production ML model implementation
• Create, manage, and execute ServiceNow change requests
• Build and maintain machine learning infrastructure
• Collaborate with data scientists and software engineers to design and implement machine learning workflows
• Implement monitoring and logging tools
• 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
• Support model development with 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
• Provide expert data PaaS on Azure storage, big data platform services, serverless architectures, Azure SQL DB, NoSQL databases, and secure automated data pipelines
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
• Experience in developing and deploying machine learning models in 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
• Experience in using Hadoop File Formats and compression techniques
• DevOps on an Azure platform
• Experience working with developer tools
• Experience with private and public cloud architectures
• Proven ability to work independently
• Proven ability to work in a team-oriented environment
• Experience with MLOps in Azure
• Azure native data/big data tools technologies and services experience
• Excellent written and oral communication and interpersonal skills
• Excellent organizational and multitasking skills
Professional Certifications:
• Azure MCSA Cloud Platform Training & Certification
• MCSD Azure Solutions Architect Training & Certification
• Multicloud experience is a plus
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