Sales - Machine Learning Engineer, AIOps

5 days ago

Singapore APPLE SOUTH ASIA PTE. LTD. Full time
Roles & Responsibilities

Summary

Imagine what you could do here. At Apple, great ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish Why Apple? At Apple, we believe our products begin with our people. By hiring a diverse team we drive creative thought. By giving that team everything they need we drive innovation. By hiring incredible engineers we drive precision. And through our collaborative process we build memorable experiences for our customers. We are looking for a highly skilled and experienced AIOps Machine Learning Engineer who has a robust understanding of Large Language Models and Generative AI to help work on exciting technologies for future Apple products and bring it to live. In this role, you will join a team of machine learning engineers with different specialization to discover and build solutions to previously-unsolved challenges and push the state of the art for global audience. You will collaborate with multi-functional teams of business SMEs, engineers, data scientists, designers, and researchers. This role is exceptionally technical, and will require you to actively engage in all aspects of the work, from conceptualization and theoretical considerations to design, coding, and implementation.

Description

In this role, you will focus on the following key areas: Model Management: Oversee the deployment, maintenance, and scaling of LLM, and services within our consumer-facing products. CI/CD: Build and maintain CI/CD pipelines to automate model train/test/deployment and scaling. Collaborative Integration: Partner with product developers, UX designers, and data scientists to ensure a seamless and intuitive integration of language models into our products. Continuous Monitoring: Build Dashboard and Regularly track model performance, ensuring consistent accuracy and reliability for consumers. Set up alerts and manage the type of monitoring needed. Feedback Integration: Develop strategies for collecting user feedback and refining the model for better alignment with consumer needs. Bias Mitigation: Proactively address and reduce potential biases in model predictions, ensuring our products are inclusive and fair. Infrastructure Management: Design and implement efficient data pipelines to support large language model training and inference Documentation: Maintain comprehensive documentation covering model versions, deployment protocols, and performance metrics. Research & Development: Stay updated with the latest trends in large language models and MLOps, ensuring our consumer products remain at the forefront of innovation.

Minimum Qualifications
  • M.S. in related field with 3+ years experience applying AIOps machine learning engineer to real business problems.
  • Language Model Expertise: Demonstrated experience working with large language models, such as OpenAI's GPT series or similar.
  • MLOps Skills: Solid background in Automating ML pipeline, including the training, testing, deployment, monitoring, and scaling of AI models.
  • Technical Toolkit: Proficiency in PyTorch, TensorFlow, Transformers, Kubernetes, Docker, LangChain, vectorDB and cloud platforms like AWS, GCP, or Azure, and Monitoring tool like Grafana, and CI/CD like airflow, gitlab, and Big Data management like Spark, Kafka.
  • Product-Centric Mindset: Proven track record of integrating AI models into consumer products, enhancing functionality, and user experience.
Preferred Qualifications
  • Communication Prowess: Excellent communication skills, with the ability to liaise between technical and non-technical teams.
  • Collaborative Spirit: Experience in cross-functional teams, ensuring seamless product development.
Tell employers what skills you have

Mitigation
TensorFlow
Machine Learning
Integration
Kubernetes
Collaboration
Azure
AWS
Documentation
Artificial Intelligence
Research and Development
monitoring
PyTorch
Engineering
Feedback
Docker
GCP
Infrastructure Management
Deployment
Testing

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