Kenvueは現在、以下求人を募集しております。
Sr. Machine Learning Engineer私たちがしていること
私たちKenvueは、日々のケアが持つ驚くべき力を信じています。100年以上の伝統と科学に根ざし、Neutrogena®, Aveeno®, Tylenol®, Listerine®, Johnson’s® and BAND-AID®など、皆様が既にご存じでご愛用いただいているアイコニックなブランドを提供しています。科学は私たちの情熱であり、ケアは私たちの才能です。
Who We Are
私たちのグローバルチームは、インサイトとイノベーションに情熱を注ぎ、最高の製品をお客様にお届けすることに全力を注ぐ、多様で優秀な22,000人以上の社員で構成されています。専門知識と共感力を備えたKenvuerであることは、毎日何百万人もの人々の生活に影響を与える力を持つことを意味します。私たちは、人を第一に考え、全身全霊をもってケアし、サイエンスで信頼を獲得し、勇気をもって解決します。私たちとあなた自身の未来を、共に切り開いていきましょう。
Role reports to:
Digital Solutions Manager場所:
Asia Pacific, India, Karnataka, Bangalore勤務地:
ハイブリッドあなたがすること
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.
o Strong understanding of Kubernetes (AKS) or API-based deployment platforms.
o Solid grasp of DevOps practices and containerization (Docker).
o 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.
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