Sr. Machine Learning Engineer- Support

Kenvue is currently recruiting for a:

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, clickhere.

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What you will do

Sr. ML Ops Engineer

Job Overview

We are seeking aSr. 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 onMicrosoft 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 usingAzure ML,Databricks, andPySpark to handle large-scale data processing and model training.

·      DevSecOps Integration: Build and maintain automatedCI/CD pipelines usingGitHub Actions, integratingSonarQube 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: UtilizeAzure Kubernetes Service (AKS) and Docker to containerize and deploy ML models, ensuring high availability and seamless scaling.

·      API Management: Design and manage robust, secureAPIs 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 fordata 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 theAzure MLOps tool stack.

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

·      Technical Proficiency: * Hands-on expertise withAzure Machine Learning andDatabricks.

o   Strong understanding ofKubernetes (AKS) or API-based deployment platforms.

o   Solid grasp ofDevOps practices and containerization (Docker).

o   Experience with code quality automation tools likeSonarQube.

·      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 asAI-900,DP-100, orAZ-305 are highly preferred.

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