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Apple Software Engineer - ML Ops India, Karnataka, Bengaluru 239248775
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Description
As a member of the Machine Learning Operations and Infrastructure engineering team, you will collaborate closely with teams throughout the organization to design, develop and deploy advanced Machine Learning and data solutions utilized in the mass production of our exceptional Apple products. You will play a pivotal role in shaping the future of our intelligent manufacturing systems.Your responsibilities will encompass designing and implementing ML/data infrastructure, platforms, and solutions tailored to our specific needs. This will involve developing infrastructure and platforms that empower others and facilitate collaboration to address a wide range of challenges within dynamic production environments.You will also be actively involved in the hands-on implementation and deployment of Machine Learning systems within factory settings, ensuring seamless integration and operation. Additionally, you will explore opportunities within our production and development processes to leverage computer vision, deep learning, LLM, and other ML/software tools to drive continuous improvements and innovation.
Minimum Qualifications
- Solid software development skills in one or more general purpose languages such as Python, Golang, C/C++ or Swift, adhering to the best coding practices.
- Demonstrated experience in designing and developing production web services using Python or Golang.
- Proficient in network infrastructure, databases, telemetry, containerization and cloud technologies.
- Familiarity with data processing and ETL technologies, including NumPy, Pandas, Amazon S3, Kafka, Airflow, Kubeflow, Dataflow, etc.
- Proven ability to translate business requirements into design concepts.
- Basic knowledge of Machine Learning concepts and frameworks.
- Minimum 3 years of relevant experience.
Preferred Qualifications
- Effective communication and collaboration skills.
- Fluency in both written and verbal English.
- Good understanding of software engineering best practices.
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