Overview
Remote
Full Time
Accepts corp to corp applications
Contract - Independent
Contract - W2
Contract - 12month(s)
Skills
RAG pipelines
FAISS
Pinecone
Milvus
LLM
Python
docker
kubernetis
Job Details
AI Engineer Data Contextualization (RAG)
Location: Remote
Focus:
- Retrieval-Augmented Generation (RAG) Systems
- We're seeking someone who understands how to contextualize organizational data for use in AI systems.
Key Expectations:
- Experience implementing RAG pipelines connecting LLMs with external or proprietary data sources.
- Proficiency in vector stores (e.g., FAISS, Pinecone, Milvus) and knowledge of RAG protocols.
- Strong grounding in data preparation, embedding generation, and indexing.
- Understanding of how to optimize retrieval relevance and context window efficiency.
General Requirements for All Roles
- Strong grounding in Python and/or modern software engineering practices.
- Experience working in cloud-native environments and with containerization (Docker, Kubernetes).
- Ability to work in fast-paced, experimental environments where proof-of-concepts and iteration cycles are common.
- Strong communication and documentation skills - capable of collaborating across engineering, data, and product teams.
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.