Kenvue - Senior MLOps Engineer - Data Modeling

Note : Women Candidates Preferred

Description :

Who we are :

At Kenvue, we realize the extraordinary power of everyday care. Built on over a century of heritage and rooted in science, were the house of iconic brands - including Neutrogena, Aveeno, Tylenol, Listerine, Johnsons and BAND-AID Brand Adhesive Bandages that you already know and love. Science is our passion; care is our talent. Our global team is made up of ~ 22,000 diverse and brilliant people, 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 the 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 futureand yours. For more information, click here.

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 : Bachelors 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.

Soft Skills :

- Exceptional problem-solving skills and the ability to thrive in a fast-
paced, collaborative environment.

Desired Qualifications :

- Architectural Mindset : Familiarity with broader solution architecture principles is a

strong plus.

- Certifications : Azure certifications such as AI-900, DP-100, or AZ-305 are highly
preferred.