MLOps Engineer
2 months ago
Role : MLOps
Exp : 5+ years
Location : Bangalore
Working days : 5 days
Mode of employment : Contract (1 year - after a year it will be extended)
Role & Responsibilities :
- Deploy and maintain machine learning models, pipelines, and workflows in production environment.
- Re-package (deployment process) ML models that have been developed in the non-production ML environment by ML Teams for deployment to the production ML environment.
- Perform the required MLOps engineering development to refactor the 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 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 our machine learning systems.
- Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
- 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.
- Provide expert data PaaS on Azure storage; big data platform services; server-less architectures; Azure SQL DB; NoSQL databases and secure, automated data pipelines.
- Work collaboratively and use sound judgment in developing robust solutions while seeking guidance on complex problems.
Basic Qualifications (Must have) :
- 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.
- Hand-on experience in Python.
- Familiarity with DevOps practices and tools such as Kubernetes, Docker, Jenkins, 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 design, development, delivery experience and deploying ETL/ELT solutions on Azure Data Factory.
- Strong analytical and problem-solving skills.
Good to Have 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.
Great-to-Have Skills :
- Azure MCSA Cloud Platform Training & Certification
- MCSD Azure Solutions Architect Training & Certification
- Multi-cloud experience is a plus.
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