AI Application Engineer

Summary

Apple is where individual imaginations gather together, committing to the values that lead to great work! Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something.

Minimum Qualifications

Bachelor's degree in Computer Science, Engineering, or related field with a minimum of 5+ years of relevant industry experience in AI application development, machine learning, or software engineering. Strong understanding of generative AI models(e.g. LLMs such as GPT, BERT) and their application in real-world solutions, such as chatbots, NLP applications, and content generation. Excellent software engineering skills, with expertise in modular, object-oriented design, and familiarity with industry-standard development processes. Proficiency in Python, PHP, Java, Git preferred.

Preferred Qualifications

3+ years of hands-on experience developing multi-agent systems or agent-based modeling, with experience in frameworks like MAS platforms, NetLogo, or AnyLogic. Proven experience in multi-agent coordination platforms (MCP) or related frameworks. Hands-on experience with cloud platforms, such as AWS, Azure, GCP, with expertise in deploying and managing scalable AI Platform. Extensive knowledge and hands-on experience with popular LLMs, such as Gemini, Claude, and GPT, including the ability to fine-tune and optimize these models for specific use cases. Solid understanding of popular AI/ML frameworks(e.g. TensorFlow, PyTorch, scikit-learn) and experience with MCP tools for multi-agent coordination and AI system integration. Curious, self-motivated, and able to drive improvements to model evaluation pipelines and annotation programs. Eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment. Outstanding communication skills – both written and verbal – with experience presenting to leadership. Comfort with ambiguity, with the ability to structure complex analysis and drive insights through data exploration and strategy research.

Description

APO BPR team is seeking a dedicated individual who is passionate about designing, implementing, and optimizing analytical solutions that deliver tangible, measurable impact within APO. As an AI application Engineer, you will collaborate closely with data scientists, business analysts, and the development team to translate cutting-edge AI technologies into real-world applications. From understanding business requirements to deploying scalable and reliable AI solutions, you will play a key role in integrating AI into practical use cases.

Responsibilities

In this role, you will AI Solution Development: Design, develop, and deploy AI-driven applications, across domains, such as machine learning, NLP, and computer vision, addressing both business requirements and end-user needs. End to End Supply Chain Connectivity: Partner with supply chain experts to understand business goals and translate into clear technical specifications and solutions recommendation. Agent Design and Development: Design and implement intelligent agents within multi-agent systems, enabling real-time collaboration based on pre-defined goals, strategies, and data exchanges. Develop agent-based models to optimize decision-making and interactions. MCP Integration: Extend and integrate Multi-Agent Coordination Platforms(MCP) to optimize resource allocation, communication, and decision-making across multiple agents in shared environments. Data Management: Architect, design, build, and maintain data pipelines that connects across internal and external supply chain systems. Enforcing data governance and control is implemented at key matrix levels. Collaboration with Data Science Teams: Collaborate with data scientists to refine algorithms, optimize models, and enhance AI performance, focusing on model tuning, feature selection, and performance benchmarking. Testing and Validation: Conduct rigorous testing and validation of AI models, including unit testing, integration testing, and A/B testing, to ensure accuracy, reliability, and scalability before deployment. Monitoring and Maintenance: Monitor deployed AI models, track performance metrics, and implement continuous improvement strategies, including model re-training and updates based on real-world data and evolving business needs.