Sr. MLOps Engineer

Description

Kenvue is currently recruiting for:

Sr.

MLOps Engineer

This position reports into Smart Manufacturing Capability Owner and is based at Bangalore.

Who We Are

At Kenvue , we realize the extraordinary power of everyday care.

Built on over a century of heritage and rooted in science, we're the house of iconic brands - including NEUTROGENA®, AVEENO®, TYLENOL®, LISTERINE®, JOHNSON'S® and BAND-AID® that you already know and love.

Science is our passion; care is our talent.

Our global team is made up with 22,000 diverse and brilliant people, passionate about insights, innovation and committed to deliver the best products to our customers.

With expertise and empathy, being a Kenvuer means to have the power to impact life of millions of people every day.

We put people first, care fiercely, earn trust with science and solve with courage - and have brilliant opportunities waiting for you! Join us in shaping our future-and yours.

For more information, click here .

What You Will Do

The Sr.

MLOps Engineer will drive the development and optimization of machine learning pipelines in a production environment.

You will be responsible for creating reusable templates for different machine learning use cases, ensuring efficient model deployment and monitoring.

This role requires hands-on experience with Azure Machine Learning, Databricks, and PySpark, as well as proficiency in managing CI/CD workflows with Bitbucket and Jenkins.

Expertise in SonarQube, AKS, API management, and model optimization are also critical.

Key Responsibilities

• Design, implement, and manage scalable machine learning (ML) pipelines using Azure ML, Databricks, and PySpark.

• Build and maintain automated CI/CD pipelines with Bitbucket and Jenkins, incorporating SonarQube to ensure code quality and security standards.

• Utilize Azure Kubernetes Service (AKS) to containerize and deploy machine learning models, ensuring high availability and scalability.

• Develop reusable templates for various ML use cases to streamline the model deployment process and enhance operational efficiency.

• Design and manage APIs to facilitate seamless interaction between ML models and other applications, ensuring robust, secure, and scalable API interfaces.

• Perform model optimization, monitor data drift, data refresh checks, and ensure the ML pipelines are cost-efficient.

• Implement cost monitoring and management strategies to ensure efficient use of resources, particularly for model training and deployment phases.

• Work closely with data scientists, DevOps, and IT teams to deploy and manage machine learning models across environments.

• Provide thorough documentation for ML workflows, pipeline templates, and optimization strategies to support cross-team collaboration.

What We Are Looking For

Required Qualifications

• Bachelor's degree in engineering, computer science, or related field.

• 4 - 6 years of total work experience, with at least 2-3 years o...