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# Generative AI Architect - Hybrid

- **Company:** Genesis10
- **Location:** Addison, TX, United States
- **Experience:** Experienced
- **Salary:** $132,454.0 - $149,094.0
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Cloud Engineering, Computer Clusters, Data Mining, Digital Assets, Elasticsearch, Python (Programming Language), OpenAI, Search Technologies, Evaluation Pipelines, Retrieval-Augmented Generation, Large Language Models, Generative AI, Fastapi, Information Technology, HuggingFace, Machine Learning Operations, Document Classification, Intelligent Document Processing
- **Published:** November 4, 2026
- **Apply:** https://www.thejobnetwork.com/job/6e094ca2-39f9-4903-943b-3b4f47da4e14/generative-ai-architect-hybrid

## About the Role

* 8+ years in software/solution architecture, with 3+ years in AI/ML or GenAI
 * Hands-on experience with LLMs, embeddings, and vector search (OpenAI, Hugging Face, Llama, Elastic Search)
 * Strong proficiency in Python (FastAPI, LangChain)
 * Experience with GPU optimization, cloud-native deployment, and MLOps
 * Strong communication skills with the ability to present to executives

Desired skills:

 * Master's degree in Computer Science, Data Science, or a related technical field
 * Experience collaborating with a diverse set of partners and stakeholders from various lines of business
 * Highly motivated, proactive, and a self-starter with a strong sense of ownership
 * Critical thinker with the ability to analyze problems, identify issues, and provide solutions
 * Ability to navigate the enterprise and data assets across multiple functions
 * Highly organized with the ability to effectively prioritize and balance multiple efforts in a fast-paced environment
 * Strong analytical and problem-solving abilities
 * Client-focused mindset

## Description

We are looking for a Generative AI Architect to design and scale a next-generation Document AI platform for the financial industry. You will lead the architecture of solutions for document classification, data extraction, and retrieval-augmented generation (RAG), ensuring performance, compliance, and auditability in a highly regulated environment., * Architect enterprise GenAI solutions for classification, extraction, and Q&A over large document sets

 * Build and optimize RAG pipelines with embeddings, vector DBs, and rerankers
 * Partner with infrastructure teams to deploy on GPU clusters (vLLM, Triton)
 * Drive model risk management: explainability, audit trails, and evaluation pipelines
 * Create architecture diagrams, technical documentation, and executive presentations
 * Collaborate with engineering, product, and compliance teams to deliver safe, scalable AI