Data Engineer (Agentic AI, LLM Training), Solutions

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 Gu0026A 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 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.

3+ 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 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