Senior Machine Learning Operations Engineer

hace 5 horas


Buenos Aires, Argentina Kimberly-Clark A tiempo completo
About the Role

We are seeking a highly skilled Senior ML Ops Engineer to join our team at Kimberly-Clark. As a key member of our Data Science and Advanced Analytics Strategy team, you will play a critical role in developing high-risk, high-reward data science solutions that will unlock future growth of analytics-based solutions across the enterprise.

Key Responsibilities
  • Design, develop, and implement machine learning applications using existing and emerging technology platforms.
  • Collaborate with Data Scientists and Data Architects to translate prototypes into scalable solutions.
  • Build, deploy, run, and monitor ML & AI solutions bridging the gap between Data Scientists and Operations.
  • Ensure that models conform to the ML strategy and guidance established.
  • Work closely with internal customers, data & analytics, and cloud team to build our next generation data science workbench and ML platform and products.
  • 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.
  • Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
  • Leverage CICD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
  • 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.
Requirements
  • Bachelor's degree in management information systems/technology, Computer Science, Engineering, or related discipline. MBA or equivalent is preferred.
  • 10+ years of experience as part of large, remote, global IT teams. 7+ years focused on development lifecycle product architecture design.
  • 5+ years proven experience in an engineering role with a focus on ML Ops, Data Engineering, ML Engineering.
  • 5+ years of experience in ML Lifecycle using Azure Kubernetes service, Azure Container Instance service, Azure Data Factory, Azure Monitor, Azure DataBricks building datasets, ML pipelines, experiments, logging, and monitoring.
  • 5+ years of experience in an execution role engaging with Data Scientists to deliver large scale analytics solutions and projects.
  • 5+ years of experience in data engineering using Snowflake.
  • Knowledge of machine learning model training, building, algorithm selection and interpretability.
  • Experience in designing, developing & scaling complex data & feature pipelines feeding ML models and evaluating their performance.
  • Proficiency in SQL and any one other programming language (e.g., R, Python, C++, Minitab, SAS, Matlab, VBA – knowledge of optimization engines such as CPLEX or Gurobi is a plus).
  • Strong experience with cloud platforms (AWS, Azure, etc.) and containerization technologies (Docker, Kubernetes).
  • Experience with CI/CD tools such as GitHub Actions, GitLab, Jenkins, or similar tools.
  • Experience with ML frameworks and libraries (TensorFlow, PyTorch, Scikit-learn).
  • Familiarity with security best practices in DevOps and ML Ops.
  • Experience in developing and maintaining APIs (e.g.: REST).
  • Agile/Scrum operating experience using Azure DevOps.
  • Experience with MS Cloud - ML Azure Databricks, Data Factory, Synapse, among others.
About Kimberly-Clark

Kimberly-Clark is a leading global consumer goods company that has been making a difference in the lives of people around the world for over 150 years. We are committed to driving innovation, growth, and impact through our products and services. Our company is built on a foundation of sustainability, inclusion, wellbeing, and career development, and we are dedicated to building technology for a purpose that matters.

We are an equal opportunity employer and welcome applications from diverse candidates. If you are passionate about machine learning and operations, and want to be part of a high-performing team that is shaping the future of data science, please apply for this exciting opportunity.



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