Senior Ml Ops Engineer

hace 3 meses

Buenos Aires, Argentina Kimberly-Clark A tiempo completo

**Your Job**

You were made to do this work: designing new technologies, diving into data, optimizing digital experiences, and constantly developing better, faster ways to get results. You want to be part of a performance culture dedicated to building technology for a purpose that matters. You want to work in an environment that promotes sustainability, inclusion, wellbeing, and career development. In your_ _**Senior **ML Ops Engineer** **role, you’ll help us deliver better care for billions of people around the world.

**About Us**

Huggies®. Kleenex®. Cottonelle®. Scott®. Kotex®. Poise®. Depend®. Kimberly-Clark Professional®. You already know our legendary brands—and so does the rest of the world. In fact, millions of people use Kimberly-Clark products every day. We know these amazing Kimberly-Clark products wouldn’t exist without talented professionals, like you.

At Kimberly-Clark, you’ll be part of the best team committed to driving innovation, growth and impact. We’re founded on 150 years of market leadership, and we’re always looking for new and better ways to perform - so there’s your open door of opportunity. It’s all here for you at Kimberly-Clark; you just need to log on

Led by Purpose. Driven by You.

Kimberly-Clark is on a mission to transform to become a data driven and AI-First company. Our enterprise vision is to embed an algorithm into every K-C decision, process, and product. To support this vision, Kimberly-Clark North America (KCNA) is investing in the growth of our high-performance **Data Science and Advanced Analytics Strategy** **team** and we are looking for entrepreneurial-minded innovators to join us in our journey.

The purpose of this agile central team is to develop high-risk, high-reward data science solutions that will unlock future growth of analytics-based solutions across the enterprise.

This newly created **individual contributor role** will report to the ML Ops Engineer Manager and will build our muscle around **machine learning** capabilities.

As ML Ops Engineer, you will work with Data Scientists and Data Architects to translate prototypes into scalable solutions. You will build, deploy, run and monitor ML & AI solutions bridging the gap between Data Scientists and Operations. You will ensure that models conform to the ML strategy and guidance established. You should be proficient at building, training, deploying, and monitoring ML models.

**Scope/Categories**:
Role will report to the ML Ops Engineer Manager.

Travel may include approximately 15% of work time.

**Key Interfaces**

Internal: Data Science Team, Data & Analytics Team, Data & Analytics Product Managers and Product Teams, D&A COE, Cloud COE, UX Designers, Web Apps Developers, Enterprise Architecture, Cyber Security Team, Legal Team.

External: Contractors, Consulting Partners, 3rd Party service providers.

**Main **Responsibilities**:

- As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers that support, build, and enable Machine Learning capabilities across the NA organization.
- You’ll work closely with internal customers, data & analytics, and cloud team to build our next generation data science workbench and ML platform and products.
- You’ll be able to further expand your knowledge and develop your expertise in modern Machine Learning frameworks, libraries and technologies while working closely with internal stakeholders to understand the evolving business needs.
- Implement scalable and reliable systems leveraging cloud-based architectures, technologies and platforms to handle model inference at scale.
- Deploy and manage machine learning & data pipelines in production environments.
- Work on containerization and orchestration solutions for model deployment.
- Participate in fast iteration cycles, adapting to evolving project requirements.
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
- Collaborate with Data scientists, software engineers, data engineers, and other stakeholders to develop and implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, monitoring, alerting and automated model deployment.
- Manage and monitor machine learning infrastructure, ensuring high availability and performance.
- Implement robust monitoring and logging solutions for tracking model performance and system health.
- Monitor real-time performance of deployed models, analyze performance data, and proactively identify and address performance issues to ensure optimal model performance.
- Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability in a timely and efficient manner.
- Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations.
- Collaborate with plat

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