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Data Engineer (Agentic AI, LLM Training), G&A Solutions Engineering (GSE) joins the iRecon Payments team to drive Agentic AI initiatives within a complex financial ecosystem. The role involves building scalable data pipelines, performing advanced feature engineering, and curating high-quality datasets specifically for training custom LLMs. You will develop upstream and downstream integrations using Model Context Protocol, Knowledge Graphs, and Vector Databases to support context engineering and retrieval. Key responsibilities include managing large-scale financial transaction data to ensure precision in reconciliation, disbursements, and receipts while translating business requirements into technical AI solutions. Required expertise includes transformer architecture, fine-tuning LLMs via PEFT/LoRA, and building multi-agent frameworks like LangChain, LlamaIndex, or AutoGen. The role focuses on modernizing legacy systems and creating GenAI-powered tools to improve productivity in high-volume transaction processing environments.
What does a Data Engineer earn?
Median $183138 from 214 postings across 58 companies.
Design and build scalable data pipelines to support Agentic AI solutions and custom LLM training.
Perform advanced feature engineering and curate high-quality datasets to optimize model performance.
Build upstream and downstream integrations with Model Context Protocol, Knowledge Graphs, and Vector Databases.
Implement Retrieval-Augmented Generation (RAG) and multi-agent frameworks like LangChain or LlamaIndex.
Process large-scale financial transaction data to ensure accuracy in reconciliation, disbursements, and receipts.
Fine-tune Large Language Models using PEFT/LoRA for specific domain tasks.
Modernize legacy data systems to integrate with new AI architectures and workflows.
What we're looking for
Bachelor's degree in Computer Science, AI, Machine Learning, or relevant work experience.
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.
3+ years of experience building production-grade AI/ML solutions in the FinTech domain.
Strong written and verbal communication skills to articulate complex technical concepts.
Demonstrated ability to modernize legacy data systems and adapt to new AI architectures.
LLM
RAG
Python
Multi-Agent Systems
Prompt Engineering
OpenAI GPT
Anthropic Claude
Google Gemini
MCP
CI/CD
AWS
GCP
Azure
Distributed Systems
APIs
Data Pipelines