Senior Machine Learning Engineer
hace 1 mes
BASF Digital Hub Madrid is a leading developer of innovative digital solutions for the chemical industry. We create new customer experiences and business growth, and drive efficiencies in processes to strengthen BASF's position as a digital leader.
We believe in creativity, trial and error, and great people working and learning together. Join our team and develop the future with us in a global team that embraces diversity and equal opportunities.
Job DescriptionWe are seeking a highly motivated and detail-oriented Machine Learning (ML) Engineer to join our dynamic team. In this role, you will be responsible for developing and maintaining the ML components. You will work closely with our senior developers to create high-quality, scalable, and responsive AI solutions.
Responsibilities- Design and build reusable ML templates and components to streamline the development of AI, ML, and Generative AI (GenAI) use cases
- Develop the LLMOps framework to extend the ML capabilities of the company to multiple teams, enabling reusability and standardization
- Collaborate with the DevOps team to develop, run, and automate CI/CD pipelines specifically for ML models and workflows
- Assist in monitoring cloud PaaS and IaaS components for availability, performance, and security as it pertains to ML models and services
- Ensure adherence to BASF compliance and security guidelines in ML and AI development
- Support and enable various AI, ML, and GenAI use cases on the DevHub Platform through effective model development and deployment
- Contribute to the improvement of internal development processes, particularly those related to ML and AI
- Machine Learning Expertise: Proficiency in ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. Ability to design, implement, and manage machine learning models. Expertise in Python.
- Generative AI: Experience with Generative AI techniques and frameworks. Ability to develop and deploy GenAI models.
- Cloud Expertise: Demonstrated experience with cloud platforms, particularly Azure. Ability to design, implement, and manage cloud infrastructure for ML and AI applications. Infrastructure as Code (IaaC): Experience with Terraform for provisioning and managing infrastructure. Ability to write and maintain modular, reusable, and scalable Terraform code.
- CI/CD Pipelines: Experience designing and implementing solutions to automate the experimentation and release cycle of machine learning, from traditional ML to LLM using CI/CD pipelines. Familiarity with GitHub Actions or similar tools is highly desirable.
- Data Engineering: Experience with data preprocessing, feature engineering, and data pipeline development.
- Kubernetes: Experience with deploying, scaling, and managing ML models in Kubernetes environments.
- Agile Methodologies: Proven experience working in Agile environments. Strong understanding of Agile principles and practices, including Scrum or Kanban.
- Product Thinking: Ability to approach problems with a product mindset, focusing on delivering value to end-users. Experience in iterative development and continuous improvement based on user feedback.
- Collaboration and Communication: Excellent interpersonal and communication skills. Ability to work collaboratively with team members, stakeholders, and clients to achieve project goals.
- Problem-Solving Skills: Strong analytical and problem-solving abilities. Capacity to troubleshoot complex technical issues and provide innovative solutions.
- Continuous Learning: Commitment to staying updated with the latest industry trends, technologies, and best practices. Eagerness to continuously improve skills and knowledge.
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