Machine Learning Engineer

1 month ago


india Allianz Services Full time

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 documentation

Requirements:

  • 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 skills
  • Demonstrated 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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