AI Engineer (LLMs + C#)
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About the Role
Job Description
Join Aperia Solutions, a leader in SaaS solutions for the Payments and Compliance industries. Aperia is a Texas-based fintech and managed consultancy firm that creates custom SaaS applications and other software-based solutions for the payments, banking, and processing industry. Founded in 1999, Aperia offers business intelligence, risk management, compliance, and customer intelligence platforms. With offices in Dallas, Washington DC, and Vietnam, Aperia is a fast-paced, global organization that strives to improve efficiency in compliance, risk, and customer service operations. Aperia’s clients include banks, processors, payment facilitators, merchant service providers, independent sales organizations, and government entities. A career at Aperia promises a great challenge, culture, and opportunities to forge your own path.
We are seeking an experienced AI/LLM Software Engineer to join our growing development team and help design, build, and integrate intelligent solutions into modern enterprise applications.
The ideal candidate has hands-on experience working with Generative AI, Large Language Models (LLMs), AI-assisted software development, and AI-powered applications. You will work closely with software engineers, architects, business stakeholders, and product teams to identify opportunities where AI can improve productivity, automation, data intelligence, and customer experiences.
This is a hands-on engineering role. Candidates should have a solid foundation in software development and APIs, with some practical experience in C#/.NET. Deep expertise in .NET, React, or Angular is not required. We are more interested in candidates who understand modern AI/LLM technologies and can apply them effectively within enterprise software environments.
Key Responsibilities:
- Design, develop, and integrate AI/LLM-powered capabilities into enterprise applications.
- Evaluate and integrate LLM platforms, models, APIs, and AI services such as Azure OpenAI, OpenAI, or similar technologies.
- Develop solutions using techniques such as:
- Prompt engineering
- Retrieval-Augmented Generation (RAG) <
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