Allianz Services

Machine Learning Engineer

Job description

Key Responsibilities:Lead end-to-end development and delivery of Machine-Learning applications, emphasizing operations and monitoring.Collaborate with multidisciplinary teams to understand business requirements, identify ML workflow requirements, improve processes, and implement new features.Perform software engineering tasks: requirements analysis, design, implementation, testing, deployment, code maintenance, etc.Develop pipelines for deployment and operation of ML applications, following CI/CD best practices.Implement optimization techniques to ensure efficient model inference, reducing computational costs and improving response times.Collaborate with Back-End Engineers to ensure the infrastructure supports the demands of the model, especially concerning GPU resources, memory, and storage.Contribute to architectural and technical documentationRequirements:Degree in a quantitative or engineering field like Computer Science, Physics, Mathematics, Statistics, etc.3-5 years of relevant professional experience in end-to-end data projects, from data cleaning to productionizing ML models.Several years of Python programming experience and overall strong software development skillsDemonstrated knowledge of CI/CD pipelines, Jenkins & GitHub actions.Solid understanding of machine learning product lifecycle and the commonly associated components (MLOps)Understanding of multiple machine learning algorithms and techniques.Expertise in ML model deployment (familiar with REST APIs) and monitoring.Experience with containerization technologies such as Docker and Kubernetes.Solid knowledge of at least one major cloud platform, preferably Azure Cloud.Experience coordinating with various technical stakeholders (Engineers, Architects, Data Scientists) to achieve common goals.Strong ability to self-organize, take ownership of topics, and drive them to delivery together with team members.Fluency in English is required; additional languages are a plus.Good to Have:Insurance knowledge and proficiency in additional languages.Experience in newer ML approaches (LLM, computer vision, etc.).

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