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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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JOB TITLE: AI/ML Engineer (GenAI), G&A Solutions Engineering (GSE)
The AI/ML Engineer (GenAI), G&A Solutions Engineering (GSE) joins the iRecon Payments team to build a next-generation payments platform. This role involves modernizing complex product architectures to enable full observability and autonomous workflows for reconciliation, invoicing, and payments. You will blend classical machine learning with Generative and Agentic AI to transform how the organization processes high-volume transactional data from sources like Apple Pay and the App Store. Key responsibilities include navigating the intersection of financial data and Large Language Models to drive operational efficiency. Required skills include experience with supervised/unsupervised learning, transformer architecture, and fine-tuning LLMs using PEFT/LoRA. You must also be proficient with RAG, MCP, and multi-agent frameworks such as LangChain, LlamaIndex, and AutoGen.
Modernize complex product architectures to enable full observability and autonomous workflows.
Build next-generation payments platforms using classical ML and Generative AI.
Process high-volume transactional data for reconciliation, invoicing, and payments.
Fine-tune Large Language Models using PEFT/LoRA for domain-specific financial tasks.
Build and extend RAG, MCP, or multi-agent frameworks like LangChain and AutoGen.
Develop conversational assistants and autonomous agents using LangGraph, CrewAI, and ReAct.
Manage the full LLM lifecycle including pre-training, SFT, and Reinforcement Learning.
Integrate emerging AI tools into existing legacy technology stacks.
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) using PEFT/LoRA for domain-specific tasks.
Proven experience building and extending RAG, MCP, or multi-agent frameworks like LangChain, LlamaIndex, or 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).
2+ years building conversational assistants or autonomous agents using advanced techniques like LangGraph, CrewAI, A2A, CoT, ReAct, or Reflection (preferred).
Experience with the full LLM lifecycle including pre-training, SFT, and Reinforcement Learning techniques (RLHF, PPO, GRPO) (preferred).