AI/ML Engineer (GenAI), G&A Solutions Engineering (GSE)

Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.

The G&A Solutions Engineering organization at Apple primarily focuses on creative ways to engineer business solutions to meet growing needs of Apple's Finance, iTunes, Sales, Retail, and Services organizations. At core, our portfolio comprises of engineered custom solutions to process high volume transactions from Apple Pay, iTunes, Ads, App Store, iPhone Activations to Sales from Retail, Online, and Resellers.

These solutions are based on cutting edge enterprise technologies ranging from Distributed Systems, Microservices, Java, Spring/Boot, Oracle, MongoDB, AWS services to AI/ML, Generative AI, and Blockchain. Accurately processing such high volume transactions is our core strength.

The iRecon Payments team is seeking a highly motivated AI/ML Engineer to help build our next-generation payments platform. In this role, you will blend classical ML with cutting-edge Generative and Agentic AI to transform how we process transactional data at scale.

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 (Model Context Protocol), or multi-agent frameworks (e.g., 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

2+ years building conversational assistants or autonomous agents using advanced techniques (LangGraph, CrewAI, A2A, CoT, ReAct, Reflection)

Experience with the full LLM lifecycle including pre-training, SFT, and Reinforcement Learning techniques (RLHF, PPO, GRPO)

Demonstrated ability to quickly master emerging AI tools and integrate them into legacy stacks

Strong written and verbal communication skills with the ability to explain complex AI concepts to business stakeholders