Data Engineer (Agentic AI, LLM Training), 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 theres 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 Apples 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 Data Engineer with a strong background in Data Science to drive our Agentic AI initiatives. In this role, you will build robust data pipelines, extract features, and curate high-quality datasets to train custom LLMs. You will navigate complex financial ecosystems to modernize data flows, ensuring accurate reconciliation, invoicing, and payments. You will play a critical role in building GenAI-powered solutions that improve user productivity and operational efficiency.Design and build scalable data pipelines to enable Agentic AI solutions and custom LLM training

Perform advanced feature engineering and dataset curation to optimize model performance

Build upstream/downstream integrations with MCP (Model Context Protocol), Knowledge Graphs, and Vector

Databases to support context engineering and retrieval (RAG)

Work with large-scale financial transaction data to ensure precision in reconciliation, disbursements, and receipts

Partner with cross-functional teams to translate business requirements into technical AI solutions2+ 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)

Bachelors degree in Computer Science, AI, Machine Learning, or relevant work experience3+ years of experience building production-grade AI/ML solutions in the FinTech domain

Strong written and verbal communication skills with the ability to articulate complex technical concepts

Demonstrated ability to modernize legacy data systems and adapt to new AI architectures

Experience with "Human-in-the-loop" data workflows for financial operations

Demonstrated ability to quickly learn and adapt to new technologies and tools