AI Dev Lead: LLM & Applied AI Solutions

AIS InfoSource; Dallas (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

AIS InfoSource, a Texas‑based fintech firm, seeks an AI Dev Lead to design and deploy AI solutions wrapping LLMs for real‑world business use. You will architect AI products, integrate with enterprise data pipelines, and ensure scalable, secure deployments across cloud environments.

Responsibilities include building AI‑driven web applications, integrating with APIs, and improving model performance through prompt engineering and data governance practices.

Qualifications

• 2+ years of AI development experience with deployed LLMs.
• Strong software engineering background and API integration skills.
• Experience with vector databases and AI workflows.

Responsibilities

• Wrap large language models for real‑world business use cases.
• Implement retrieval augmented generation with vector databases.
• Build and maintain API integrations between AI providers and systems.
• Structure SQL/NoSQL databases for knowledge retrieval and history.
• Collaborate with frontend engineers for AI chatbots and dashboards.
• Perform prompt engineering, evaluation, and tuning for reliability.
• Manage corpus ingestion and vectorization for proprietary data.
• Ensure data governance, security, and privacy in AI workloads.
• Document architecture decisions and support agile sprints.

Skills

AI development

Python

LLM APIs

RAG architecture

REST APIs

SQL databases

LangChain

Cloud platforms

Docker/Kubernetes

Web front ends

Embedding models

ETL pipelines

AI/ML certification

Git CI/CD

Education

Bachelor’s or Master’s degree in CS/AI/Data Eng

Tools

OpenAI API

Azure OpenAI

Hugging Face

RESTful APIs

Docker

Kubernetes

Job description

AIS InfoSource, a Texas‑based fintech firm, seeks an AI Dev Lead to design and deploy AI solutions wrapping LLMs for real‑world business use. You will architect AI products, integrate with enterprise data pipelines, and ensure scalable, secure deployments across cloud environments.

Responsibilities include building AI‑driven web applications, integrating with APIs, and improving model performance through prompt engineering and data governance practices.