MLops Engineer Bangalore Contractual

1 month ago

Bengaluru, India Promaynov Full time

Position Title: Senior ociate Manager MLOps Engineer

Purpose:

The MLOps engineers role is service focused and will create data pipeline and engineering infrastructure to support our enterprise machine learning systems. This role will collaborate with data scientists and statisticians from various functions to facilitate and lead scientific and/or business knowledge discovery insights and forecasting. The MLOps engineer will be part of a highly collaborative and crossfunctional team of technology and data experts working on solving complex scientific and business challenges using cutting edge data and ytics technologies. The MLOps Engineer will be responsible for designing implementing and maintaining machine learning infrastructure pipelines and workflows. This role will require a deep understanding of data management software development and cloud computing. The successful candidate 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.

The MLOps engineer is expected to work with teams spread globally across different time zones.

Role & Responsibilities

Deploy and maintain machine learning models pipelines and workflows in production environment.

Repackage (deployment process) ML models that have been developed in the nonproduction ML environment by ML Teams for deployment to the production ML environment.

Perform the required MLOps engineering development to refactor the nonproduction 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 doentation 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.

Work collaboratively and use sound judgment in developing robust solutions while seeking guidance on complex problems.

Basic Qualifications (Must have)

Bachelors or masters 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.

Handon 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 cloudbased systems using APIs.

Experience developing and maintaining ML systems built with opensource 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 ytical and problemsolving ss.

Good to Have Ss:

Cloud migration odologies 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 GitLabs 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 teamoriented environment and work collaboratively in a problemsolving environment.

Experience with MLOps in Azure preferred.

Azure native data/bigdata tools technologies and services experience including Storage BLOBS ADLS Azure SQL DB and SQL Data Warehouse.

Excellent written and oral communication and interpersonal ss.

Excellent organizational and multitasking.

GreattoHave Ss:

Azure MCSA Cloud Platform Training & Certification

MCSD Azure Solutions Architect Training & Certification

Multicloud experience is a plus.

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