Overview
Full Time
Part Time
Accepts corp to corp applications
Contract - W2
Contract - Independent
Skills
AWS S3
AZURE BLOB
JIRA
AI
GIT
CI/CD
AGILE METHODOLOGIES
TYPING
Job Details
Job Title: Generative AI Architect (100% Remote)
About the Role
We are seeking an experienced Generative AI Architect to design, build, and scale cutting-edge AI solutions leveraging Large Language Models and retrieval-augmented generation (RAG) pipelines. This is a fully remote position with flexibility and the opportunity to work on impactful, production-grade GenAI systems.
Key Responsibilities
Architect and implement end-to-end Generative AI solutions, including LLM fine-tuning, prompt engineering, and agentic workflows Design and optimize retrieval-augmented generation (RAG) systems with advanced chunking, embedding strategies, and hybrid search Build and maintain scalable LLM chains, orchestrators, and multi-agent frameworks Develop robust data ingestion pipelines for unstructured content (PDFs, docs, images, etc.) with intelligent parsing, segmentation, and metadata extraction Integrate cloud storage (Azure Blob / AWS S3) and document management systems Manage vector databases (PGVector, Weaviate, Pinecone, etc.) and implement efficient similarity search and re-ranking strategies Collaborate with cross-functional teams using Agile methodologies (Jira), Git for version control, and CI/CD best practices
Required Skills & Experience
If you're passionate about pushing the boundaries of what's possible with Generative AI and have a track record of shipping robust, scalable LLM applications, we'd love to hear from you!
About the Role
We are seeking an experienced Generative AI Architect to design, build, and scale cutting-edge AI solutions leveraging Large Language Models and retrieval-augmented generation (RAG) pipelines. This is a fully remote position with flexibility and the opportunity to work on impactful, production-grade GenAI systems.
Key Responsibilities
Architect and implement end-to-end Generative AI solutions, including LLM fine-tuning, prompt engineering, and agentic workflows Design and optimize retrieval-augmented generation (RAG) systems with advanced chunking, embedding strategies, and hybrid search Build and maintain scalable LLM chains, orchestrators, and multi-agent frameworks Develop robust data ingestion pipelines for unstructured content (PDFs, docs, images, etc.) with intelligent parsing, segmentation, and metadata extraction Integrate cloud storage (Azure Blob / AWS S3) and document management systems Manage vector databases (PGVector, Weaviate, Pinecone, etc.) and implement efficient similarity search and re-ranking strategies Collaborate with cross-functional teams using Agile methodologies (Jira), Git for version control, and CI/CD best practices
Required Skills & Experience
- Deep hands-on experience with Small, Medium, and Large Language Models (e.g., GPT-4, Claude, Llama 3, Mistral, BERT, etc.) and modern GenAI frameworks (LangChain, LlamaIndex, Haystack, Semantic Kernel, etc.)
- Proven expertise in building production RAG systems and LLM orchestrators/agentic workflows
- Advanced Python proficiency (including async, typing, pydantic, etc.)
- Strong experience with Git workflows and Jira
- Hands-on work with cloud storage solutions: Azure Blob Storage or AWS S3
- Expertise in document processing: PDF parsing (PyMuPDF, Unstructured, LlamaParse, etc.), intelligent segmentation, and multimodal content handling
- Solid experience with vector databases (PGVector, Weaviate, Pinecone, Qdrant, etc.) and vector search optimization
- Familiarity with evaluation frameworks (RAGAS, DeepEval, etc.) and LLMOps practices is a strong plus
If you're passionate about pushing the boundaries of what's possible with Generative AI and have a track record of shipping robust, scalable LLM applications, we'd love to hear from you!
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.