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About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
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As an AI/ML Engineer at Apple’s G&A Solutions Engineering team, you will join the iRecon Payments team to develop next-generation payment solutions by integrating classical machine learning techniques with advanced Generative and Agentic AI. Your primary responsibilities include modernizing product architectures for full observability in reconciliation, invoicing, and payments processes, leveraging financial data and Large Language Models (LLMs) to enhance operational efficiency. You will need extensive experience in building ML solutions using supervised/unsupervised learning, transformer architecture, and fine-tuning LLMs with PEFT/LoRA. Additionally, familiarity with RAG, MCP frameworks, and multi-agent systems like LangChain is essential. This role requires a strong background in AI/ML, ideally within the FinTech domain, along with expertise in deploying production-grade solutions and integrating emerging AI tools into existing systems.
Develop next-generation payments platform by integrating classical ML with Generative and Agentic AI.
Modernize product architectures for full observability in reconciliation, invoicing, and payment workflows.
Fine-tune Large Language Models (LLMs) using PEFT/LoRA for specific financial tasks.
Build and extend RAG, MCP, or multi-agent frameworks to enhance transactional data processing.
Navigate the intersection of financial data and LLMs to improve operational efficiency.
Deploy production-grade AI/ML solutions in FinTech domain to process high-volume transactions.
What we're looking for
2+ years of experience building machine learning solutions using supervised/unsupervised learning, classification, recommendation systems, and clustering algorithms.
In-depth knowledge of transformer architecture, LLMs, and Agentic AI concepts.
Hands-on experience fine-tuning Large Language Models (LLMs) for domain-specific tasks.
Proven experience building and extending RAG, MCP, or multi-agent frameworks like LangChain, LlamaIndex, AutoGen.
Bachelor's degree in Computer Science, AI, Machine Learning, or relevant work experience.
3+ years deploying production-grade AI/ML solutions in the FinTech domain preferred.
Experience with full LLM lifecycle including pre-training, SFT, and Reinforcement Learning techniques.