Senior MLOps Engineer
hace 1 mes
About the Role:
We are seeking a highly skilled Senior MLOps Engineer to join our dynamic Data & Analytics team at ZF Group. In this role, you will be responsible for the end-to-end deployment of machine learning models, building mockup web applications, and providing support in infrastructure engineering and testing of GenAI models.
Key Responsibilities:
- End-to-End Model Deployment: Take ownership of the entire machine learning model deployment process, from development to production. This includes preprocessing, training, evaluation, packaging, and deployment of models.
- Web Application Development: Build mockup web applications using frameworks like Shiny (RShiny or PyShiny) to showcase the functionality and visualization of machine learning models. Develop user-friendly interfaces for interacting with the models.
- Testing and Quality Assurance: Develop and execute testing strategies to ensure the reliability, performance, and accuracy of deployed models and applications. Implement monitoring and logging solutions to track model performance and user interactions.
- GenAI Model Support: Provide support for engineering, testing, and deployment of Generative Artificial Intelligence (GenAI) foundation models, such as OpenAI, Llama2, Claude2. Assist in data preparation, model training, and evaluation.
- Continuous Integration/Continuous Deployment (CI/CD): Implement CI/CD pipelines to automate the deployment process of machine learning models and web applications. Ensure reliable and efficient delivery of updates and improvements.
- Collaboration and Communication: Collaborate with data scientists, software engineers, and business stakeholders to understand requirements, prioritize tasks, and deliver solutions that meet business needs. Communicate technical concepts and solutions effectively to non-technical stakeholders.
- Documentation and Knowledge Sharing: Document deployment processes, infrastructure configurations, and best practices. Share knowledge and expertise with team members through documentation, presentations, and training sessions.
- Stay Updated: Stay abreast of developments in machine learning deployment, web development, and infrastructure engineering. Continuously learn and explore new tools, technologies, and techniques to improve processes and workflows.
Requirements:
- Degree in Computer science, engineering, or a related field.
- Proven experience in machine learning model deployment, web application development, and infrastructure engineering.
- Proficiency in programming languages such as Python, R, Pyspark.
- Experience with machine learning frameworks and libraries.
- Strong understanding of cloud computing services (especially, AWS) and containerization technologies (e.g., Docker, Kubernetes) or alternatives.
- Familiarity with BI tools such as PowerBI, Tableau, Qlik, Looker is beneficial.
- Excellent problem-solving skills and attention to detail.
- Ability to thrive in a collaborative team environment and communicate technical concepts effectively to non-technical stakeholders.
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