Sr. Machine Learning Engineer

Kenvue Inc.

Bengaluru

On-site

INR 4,500,000 - 6,500,000

Full time

14 days+

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Job summary

Kenvue Inc. in Bengaluru, India, offers a hybrid role for a Sr. ML Ops Engineer to design, automate, and manage the lifecycle of ML models on Azure. You will build scalable pipelines and containerized deployments, and craft secure APIs for production systems.

The role requires 5+ years of Azure MLOps experience, hands-on work with Azure ML and Databricks, and strong DevOps with Docker, Kubernetes, and SonarQube. Collaboration with Data Science, DevOps, and IT teams is essential.

Qualifications

Bachelor’s degree in engineering, computer science, or a related field.
5+ years of total experience focusing on the Azure MLOps tool stack.
Proven track record deploying ML models in high-scale production.
Hands-on expertise with Azure Machine Learning and Databricks.
Strong Kubernetes (AKS) or API-based deployment experience.
DevOps practices and containerization (Docker); SonarQube experience.
Excellent problem-solving and collaboration skills.

Responsibilities

Pipeline Architecture & Automation: design end-to-end ML pipelines using Azure ML, Databricks, and PySpark.
CI/CD: build automated pipelines with GitHub Actions and SonarQube for quality and security.
Reusable templates: develop modular deployment templates for ML use cases.
Deployment & Orchestration: containerize models with AKS and Docker, ensure high availability.
API management & system architecture: design robust APIs for ML models and downstream apps.
Governance: monitor data drift, manage model lifecycle, control costs.
Collaboration: liaise between data scientists, DevOps, and IT for smooth transitions.

Skills

Pipeline design & automation

DevSecOps integration

Cross-functional collaboration

Cost governance awareness

Education

Bachelor’s degree in engineering or CS

Tools

Azure ML

Databricks

Kubernetes (AKS)

Docker

GitHub Actions

SonarQube

Job description

Sr. Machine Learning Engineer- Support

What we do

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.

Who We Are

Our global team is ~ 22,000 brilliant people with a workplace culture where every voice matters, and every contribution is appreciated.We are passionate about insights, innovation and committed to delivering the best products to our customers. With expertise and empathy, being a Kenvuer means having the power to impact 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.

Role reports to:

Digital Solutions Manager

Location:

Asia Pacific, India, Karnataka, Bangalore

Work Location:

Hybrid

What you will do

Sr. ML Ops Engineer

Job Overview

We are seeking a Sr. MLOps Engineer with 5+ years of experience to design, automate, and manage the lifecycle of machine learning models. This role is focused on building high-performance, scalable ML infrastructure on Microsoft Azure that bridges the gap between data science and production-grade engineering. You will be responsible for creating a \"Plug-and-Play\" deployment framework that ensures our ML solutions are resilient, secure, and cost-optimized.

Key Responsibilities

1. Pipeline Architecture & Automation
- Scalable ML Pipelines: Design and manage end-to-end ML pipelines using Azure ML, Databricks, and PySpark to handle large-scale data processing and model training.
- DevSecOps Integration: Build and maintain automated CI/CD pipelines using GitHub Actions, integrating SonarQube to enforce strict code quality and security standards.
- Reusable Frameworks: Develop modular templates for various ML use cases to streamline deployment and drive operational efficiency across the enterprise.

2. Deployment & Orchestration
- Containerization: Utilize Azure Kubernetes Service (AKS) and Docker to containerize and deploy ML models, ensuring high availability and seamless scaling.
- API Management: Design and manage robust, secure APIs to facilitate seamless interactions between ML models and downstream applications.
- Solution Architecture: Understand and contribute to the overall system architecture to ensure ML components are modular and scalable.

3. Optimization & Governance
- Model Lifecycle Management: Perform model optimization, monitor for data drift, and implement automated data refresh checks to maintain model accuracy.
- Cost Engineering: Implement cost-monitoring strategies to ensure efficient resource utilization during high-compute training and deployment phases.
- Documentation: Provide detailed technical documentation for workflows, pipeline templates, and optimization strategies to ensure long-term maintainability.

4. Collaboration
- Cross-Functional Synergy: Act as the technical liaison between Data Scientists, DevOps, and IT teams to ensure smooth model transitions across Dev, QA, and Production environments.

Required Qualifications

Education: Bachelor’s degree in engineering, Computer Science, or a related field.

Experience: 5+ years of total experience with a deep focus on the Azure MLOps tool stack.

Production Mastery: Proven track record of deploying and maintaining ML models in high-scale production environments.

Technical Proficiency:
- Hands-on expertise with Azure Machine Learning and Databricks.
- Strong understanding of Kubernetes (AKS) or API-based deployment platforms.
- Solid grasp of DevOps practices and containerization (Docker).
- Experience with code quality automation tools like SonarQube.