Machine Learning Engineer
4 weeks ago
Are you a skilled machine learning engineer looking for a new challenge? Do you have experience in deploying and maintaining machine learning models in production environments? We are seeking a Senior Associate Manager – MLOps Engineer to join our team at Promaynov Executive Trainers LLP.
Key Responsibilities:- Deploy and maintain machine learning models, pipelines, and workflows in production environments.
- Re-package ML models for deployment to production environments.
- Perform MLOps engineering development to refactor non-production ML model implementations.
- Create and manage ServiceNow change requests for model deployments.
- Build and maintain scalable, reliable, and efficient machine learning infrastructure.
- Collaborate with data scientists and software engineers to design and implement machine learning workflows.
- Implement monitoring and logging tools for optimal model performance.
- Identify and evaluate new technologies to improve machine learning system performance and maintainability.
- 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.
- 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 design, development, delivery experience, and deploying ETL/ELT solutions on Azure Data Factory.
- Strong analytical and problem-solving skills.
- Cloud migration methodologies and processes, including tools like Azure Data Factory, Event Hub, etc.
- Experience in using Hadoop File Formats and compression techniques.
- DevOps on an Azure platform.
- Experience working with Developer tools such as Visual Studio, GitLab's, 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 multi-tasking skills.
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