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Engaged Employer

Zetwerk Manufacturing Businesses

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4.1

based on 777 Reviews

1 Zetwerk Manufacturing Businesses Engineer Job

MLOps Engineer -ML Platforms

Zetwerk

4.1

based on 777 Reviews

3-8 years

Hyderabad / Secunderabad, Chennai, Bangalore / Bengaluru

1 vacancy

MLOps Engineer -ML Platforms

Zetwerk Manufacturing Businesses

posted 11hr ago

Job Role Insights

Flexible timing

Key skills for the job

Python SQL Cloud Computing Automation Testing Machine Learning Project Management

+ 3 more

Job Description

MLOPS Lead-

Experience - 3.6 tp 8 Years

Location : Chennai / Bangalore / Hyderabad

As a Senior MLOps Engineer, the role requires hands-on experience in building and managing ML Systems in Production with excellent MLOPS Skill sets. The position would be responsible for building machine learning pipelines and ML Application services and providing machine learning engineering support for Production teams .

This role operates at the crossroads of Machine Learning, Software/Data Engineering, and DevOps, leveraging best practices from each domain to optimize the deployment and management of machine learning models in production environments.

The Position would also be expected to be able to enhance the Technology stack, focusing on continuous improvements in the reliability, automation and efficiency of the Platform Solutions.

Roles and responsibilities

Manage ML Platform infrastructure to automate and accelerate model development and deployment
Define and build scalable ML pipelines that enables data scientists to build better models
Build dashboards and other Monitoring tools to optimize performance and Infrastructure Costs
Monitor and provide support to infrastructure and in production systems
Identify possible issues and performance leakages in the system and perform optimization
Mentoring team members through code review and knowledge sharing
Lead, mentor, and manage a team of Mlops engineers, fostering a collaborative and high-performance environment.
Oversee the team s project management, ensuring timely and successful delivery of data solutions.
Design, develop, and maintain data science models and algorithms along with the DS team to drive business insights and decision-making onto the ML Platforms
Implement and manage CI/CD pipelines of Core Solution Packages and Applications
Containerize applications and workflows using Docker and App Service for consistent environments and deployment.
Work closely with various teams, including data science, engineering, and business units, to align ML Platform Solution with client goals.
Facilitate communication and collaboration between team members and other departments to ensure cohesive project execution.
Provide technical support and insights to cross-functional teams as needed.
Ensure ML systems comply with security standards and best practices in a cloud environment.
Identify and address technical debt in current ML projects, incorporating best MLOps practices.

Preferred Experience

8 + years of hands-on experience at scale in data science / ML engineering
Excellent hands on skills to write clean and structured SQL, Python and Shell programs
Good experience with infrastructure, including Cloud Computing, Linux OS, Networks, Kubenetes, Docker, Infrastructure as Code, RDBMS and NoSQL Databases
Good understanding of Machine learning concepts and MLops Best practices.
Quality experience in serving real-time, production-level machine learning models
Proven experience leading and Managing a team
Excellent communication skills
5+ years hands-on experience with Azure Cloud Technologies such as Azure Databricks, Azure DevOps, Azure App Service, Docker, Azure Key Vault, and other managed Azure services.
Proficient in AWS technologies, including Athena, Glue, ECS, EKS, and VPC, as well as AWS SageMaker for deploying machine learning models, improving automation, and implementing essential checks.
Proficiency in Python for developing automation Scripts, and Pipelines
Familiarity with Infrastructure as Code (IaC) tools such as Terraform or AWS CloudFormation for managing cloud infrastructure.
Basic Familiarity with the Data Science and Machine Learning lifecycle, as well as frameworks like scikit-learn, PyTorch, and TensorFlow.