AI Application Engineer

Apple

16h

About The Position

Apple’s APO BPR team is seeking a dedicated engineer passionate about designing, implementing, and optimizing analytical solutions that deliver measurable impact within APO. The AI Application Engineer collaborates with data scientists, business analysts, development teams, and supply chain experts to turn business requirements and AI technologies into practical, scalable applications. The role develops AI-driven solutions across areas such as machine learning, natural language processing, and computer vision. It also involves designing intelligent agents and multi-agent systems, integrating multi-agent coordination platforms, building data pipelines across supply chain systems, and supporting data governance. The engineer works with data science teams to improve models, tests and validates solutions before deployment, and monitors deployed models to maintain performance and support continuous improvement.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related field.
At least 5 years of relevant industry experience in AI application development, machine learning, or software engineering.
Strong understanding of generative AI models, including LLMs such as GPT and BERT, and their use in practical solutions such as chatbots, NLP applications, and content generation.
Strong software engineering skills, including modular and object-oriented design, and familiarity with industry-standard development processes.
Proficiency in Python, PHP, Java, and Git is preferred.
At least 3 years of hands-on experience developing multi-agent systems or agent-based modeling.
Experience with frameworks such as MAS platforms, NetLogo, or AnyLogic.
Proven experience with multi-agent coordination platforms (MCP) or related frameworks.
Hands-on experience with cloud platforms such as AWS, Azure, or GCP, including deploying and managing scalable AI platforms.
Extensive knowledge and hands-on experience with LLMs such as Gemini, Claude, and GPT, including fine-tuning and optimization for specific use cases.
Understanding of AI/ML frameworks such as TensorFlow, PyTorch, and scikit-learn, and experience with MCP tools for multi-agent coordination and AI system integration.
Curiosity, self-motivation, and ability to drive improvements to model evaluation pipelines and annotation programs.
Ability and eagerness to learn new skills and solve dynamic problems.
Outstanding written and verbal communication skills, including experience presenting to leadership.
Comfort with ambiguity and ability to structure complex analysis and develop insights through data exploration and strategy research.

Responsibilities

Design, develop, and deploy AI-driven applications across machine learning, NLP, computer vision, and other domains to address business requirements and end-user needs.
Partner with supply chain experts to understand business goals and translate them into technical specifications and solution recommendations.
Design and implement intelligent agents in multi-agent systems, enabling real-time collaboration through defined goals, strategies, and data exchanges.
Develop agent-based models to optimize decision-making and interactions.
Extend and integrate multi-agent coordination platforms (MCP) to optimize resource allocation, communication, and decision-making across agents in shared environments.
Architect, design, build, and maintain data pipelines connecting internal and external supply chain systems, and implement data governance and controls at key matrix levels.
Collaborate with data scientists to refine algorithms, tune models, select features, and benchmark performance.
Conduct unit, integration, and A/B testing to validate AI models for accuracy, reliability, and scalability before deployment.
Monitor deployed AI models, track performance metrics, and implement continuous improvement, including retraining and updates based on real-world data and changing business needs.
Drive improvements to model evaluation pipelines and annotation programs.