3 Days Left: Machine Learning Engineer
2 weeks ago
- 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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