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Senior GenAI Developer

This role is for a Senior GenAI Developer in Irving, TX, on a contract basis, requiring expertise in Prompt Engineering and RAG Architecture. Pay rate is unspecified. Key skills include Node.js/JavaScript/TypeScript, AI model deployment, and data modeling.
🌎 - Country
United States
💱 - Currency
$ USD
💰 - Day rate
Unknown
Unknown
🗓️ - Date discovered
February 21, 2025
🕒 - Project duration
Unknown
🏝️ - Location type
On-site
📄 - Contract type
Unknown
🔒 - Security clearance
Unknown
📍 - Location detailed
Irving, TX
🧠 - Skills detailed
#Microservices #TypeScript #Data Modeling #Deployment #Databases #Data Pipeline #Cloud #Scala #SQL (Structured Query Language) #AI (Artificial Intelligence) #Model Deployment #JavaScript
Role description
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Job Title: Senior GenAI Developer with Prompt Engg with RAG arch exp

Location: Irving TX (2/3 days onsite)

Contract

Job Description:

We are seeking a Senior GenAI Developer with expertise in Prompt Engineering, RAG Architecture, AI-driven applications, and Language Models. The ideal candidate should have a strong backend development background with hands-on experience in Node.js/ JavaScript/TypeScript.

Key Responsibilities:
• Design and implement RAG (Retrieval-Augmented Generation) architectures for AI applications.
• Develop and optimize AI prompts and prompt engineering strategies for improved model responses.
• Work with language models (LLMs) to fine-tune and integrate into applications.
• Build and maintain scalable backend systems to support AI-driven features.
• Implement APIs and microservices for seamless AI model integration.
• Optimize and enhance GenAI pipelines for performance and efficiency.

Required Skills & Qualifications:
• Expertise in Prompt Engineering and AI Prompt Optimization.
• Strong knowledge of RAG architecture and LLM fine-tuning.
• Proficiency in Node.js or JavaScript or TypeScript for backend development.
• Experience with AI model deployment, APIs, and cloud-based AI services.
• Experience in Data Modeling, SQL, and Data Pipelines.
• Knowledge of Vector Databases, Embeddings, and AI-driven search.
• Familiarity with MLOps, CI/CD for AI models, and performance optimization.