Analyst, AI Solutions, ML Ops

at  The Este Lauder Companies

ABOUT ESTÉE LAUDER COMPANIES

The Estée Lauder Companies is the global leader in prestige beauty — delighting consumers with transformative products and experiences, inspiring them to express their individual beauty. We are the only company focused solely on prestige makeup, skincare, fragrance, and hair care with a diverse portfolio of 25+ brands sold in approximately 150 countries and territories. Infused throughout our organization is a passion for creativity and imagination — a desire to push the boundaries and invent the unexpected — as we continue the bold work of our founder Estée Lauder.

JOB REQUIREMENTS AND QUALIFICATIONS

• Bachelor’s degree in Computer Science, Engineering, or a related field or equivalent experience.
• 2+ years of experience in machine learning operations, DevOps, or related fields.
• Proficiency in programming languages such as Python and experience with ML frameworks like TensorFlow, PyTorch, or similar.
• Strong understanding of cloud platforms (Azure, AWS, Google Cloud) and containerization technologies (Docker, Kubernetes).
• Experience with CI/CD tools and practices.
• Excellent problem-solving skills and the ability to work independently and collaboratively.
• Strong communication skills to effectively convey technical concepts to non-technical stakeholders.
• Familiarity with data security and compliance requirements in machine learning environments.
  Job: Information Technology
  Primary Location: RO-B-Bucharest
  Job Type: Standard
  Schedule: Full-time
  Shift: 1st (Day) Shift
  Job Number: 241492

WHAT YOU’LL DO

The ML Ops Engineer will be responsible for managing the deployment, monitoring, and maintenance of machine learning models in production environments. Reporting to the AI Solutions Manager, this role focuses on ensuring the scalability, reliability, and efficiency of AI solutions across the organization. The ideal candidate will have a strong background in machine learning operations, cloud infrastructure, and DevOps practices.

KEY ROLES AND RESPONSIBILITIES

• Model Deployment: Design and implement scalable deployment pipelines for machine learning models, ensuring seamless integration with existing systems and applications.
• Monitoring and Maintenance: Develop monitoring and alerting solutions to track model performance and operational metrics in real-time. Implement strategies for model retraining and updates based on performance feedback.
• Infrastructure Management: Manage cloud-based infrastructure and resources to support machine learning workloads. Optimize resource utilization and ensure cost-effectiveness.
• Collaboration: Work closely with data scientists, AI developers, and IT teams to facilitate the transition of models from development to production. Ensure alignment with business and technical requirements.
• Security and Compliance: Implement best practices for data security, privacy, and compliance in all ML Ops processes. Ensure adherence to industry standards and regulations.
• Automation: Develop automated workflows for continuous integration and continuous deployment (CI/CD) of machine learning models. Streamline processes to improve efficiency and reduce manual intervention.
• Documentation: Maintain comprehensive documentation of ML Ops processes, system architectures, and deployment configurations. Provide training and support to team members on ML Ops best practices.
  Qualifications