Sr. Machine Learning Engineer- Support

Job description

Kenvue is currently recruiting for a: 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&#43; 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 &#34;Plug-and-Play&#34; 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. ·       <