Enter a job title or keyword
Data Engineer (Agentic AI, LLM Training), G&A Solutions Engineering (GSE)
Job Location:
Job Location:
Austin, TX - USA
Monthly Salary:
Not provided by the employer
Monthly Salary:
Posted:
3 July 2026 (30+ days ago)
Posted:
Application Deadline:
30 September 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
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 Gu0026A 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 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 trainingnnPerform advanced feature engineering and dataset curation to optimize model performancennBuild upstream/downstream integrations with MCP (Model Context Protocol) Knowledge Graphs and VectornnDatabases to support context engineering and retrieval (RAG)nnWork with large-scale financial transaction data to ensure precision in reconciliation disbursements and receiptsnnPartner with cross-functional teams to translate business requirements into technical AI solutions
2 years of experience building machine learning solutions using supervised/unsupervised learning classification recommendation systems and clustering algorithmsnnIn-depth knowledge of transformer architecture LLMs and Agentic AI conceptsnnHands-on experience fine-tuning Large Language Models (LLMs) using PEFT/LoRA for domain-specific tasksnnProven experience building and extending RAG MCP (Model Context Protocol) or multi-agent frameworks (e.g. LangChain LlamaIndex AutoGen)nnBachelors degree in Computer Science AI Machine Learning or relevant work experience
3 years of experience building production-grade AI/ML solutions in the FinTech domainnnStrong written and verbal communication skills with the ability to articulate complex technical conceptsnnDemonstrated ability to modernize legacy data systems and adapt to new AI architecturesnnExperience with Human-in-the-loop data workflows for financial operationsnnDemonstrated ability to quickly learn and adapt to new technologies and tools
The iRecon Payments team is seeking a highly motivated Data Engineer with a strong background in Data Science to drive our Agentic AI 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 trainingnnPerform advanced feature engineering and dataset curation to optimize model performancennBuild upstream/downstream integrations with MCP (Model Context Protocol) Knowledge Graphs and VectornnDatabases to support context engineering and retrieval (RAG)nnWork with large-scale financial transaction data to ensure precision in reconciliation disbursements and receiptsnnPartner with cross-functional teams to translate business requirements into technical AI solutions
2 years of experience building machine learning solutions using supervised/unsupervised learning classification recommendation systems and clustering algorithmsnnIn-depth knowledge of transformer architecture LLMs and Agentic AI conceptsnnHands-on experience fine-tuning Large Language Models (LLMs) using PEFT/LoRA for domain-specific tasksnnProven experience building and extending RAG MCP (Model Context Protocol) or multi-agent frameworks (e.g. LangChain LlamaIndex AutoGen)nnBachelors degree in Computer Science AI Machine Learning or relevant work experience
3 years of experience building production-grade AI/ML solutions in the FinTech domainnnStrong written and verbal communication skills with the ability to articulate complex technical conceptsnnDemonstrated ability to modernize legacy data systems and adapt to new AI architecturesnnExperience with Human-in-the-loop data workflows for financial operationsnnDemonstrated ability to quickly learn and adapt to new technologies and tools
Required Experience:
Unclear Seniority
About Company
Ask Siri to name the most successful company in the world and it might respond: Apple. And it's not just out of familial pride. Apple consistently ranks highly in profit, revenue, market capitalization, and consumer cachet. In 2018, the company became the first reach a trillion dollar ... View more
