Sr. Machine Learning Engineer

Sr. Machine Learning Engineer

Lo que hacemos

En Kenvue, nos damos cuenta del extraordinario poder del cuidado diario. Sobre la base de más de un siglo de herencia y arraigados en la ciencia, somos el hogar de marcas icónicas, incluidas NEUTROGENA®, AVEENO,® TYLENOL,® LISTERINE,® JOHNSON'S® y BAND-AID® que ya conoces y amas. La ciencia es nuestra pasión; El cuidado es nuestro talento.

Quiénes somos

Nuestro equipo global está formado por ~ 22.000 personas brillantes con una cultura laboral en la que cada voz importa y cada contribución es apreciada. Nos apasionan las ideas, innovación y comprometidos con la entrega de los mejores productos a nuestros clientes. Con experiencia y empatía, ser un Kenvuer significa tener el poder de impactar a millones de personas todos los días. Ponemos a las personas en primer lugar, nos preocupamos ferozmente, nos ganamos la confianza de la ciencia y resolvemos con coraje, ¡y tenemos oportunidades brillantes esperándote! Únase a nosotros para dar forma a nuestro futuro y al suyo. Para obtener más información, haga clic en aquí.

Role reports to:

Digital Solutions Manager

Ubicación:

Asia Pacific, India, Karnataka, Bangalore

Lugar de trabajo:

Híbrido

What will you

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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