Context / Retrieval Engineer (RAG Specialist)

DeWinter Group; Campbell (CA); Remote; USD 68,880 - 241,080; Part time

Job summary

A leader in AI solutions is seeking a skilled Context/Retrieval Engineer for a 12-month contract. This remote role focuses on optimizing Retrieval-Augmented Generation (RAG) pipelines to enhance accuracy in AI responses. Candidates must have at least 3 years of experience in Information Retrieval or AI Engineering, along with expertise in vector databases like Pinecone. Strong Python skills and autonomy are required, as the position involves delivering impactful results quickly.

Qualifications

• 3+ years of experience in Information Retrieval or AI Engineering.
• Deep expertise in Vector Databases and LangChain/LlamaIndex.
• Demonstrated ability to work autonomously.

Responsibilities

• Optimize Retrieval-Augmented Generation (RAG) pipelines.
• Implement chunking strategies and metadata tagging.
• Select and fine-tune embedding models.

Skills

Information Retrieval

AI Engineering

Vector Databases

Python

SQL

Natural Language Processing

Communication Skills

Tools

Pinecone

Chroma

Milvus

LangChain

LlamaIndex

Job description

Title: Context/Retrieval Engineer
Job Type: Contract
Contract Length: 12 Months
Pay Range: $50/hr – $175/hr
Start Date: ASAP
Location: Remote

About the Opportunity

Our client, a leader in AI testing and Generative AI solutions, is looking for a skilled Context/Retrieval Engineer (RAG Specialist) to join their team for a 12-month engagement. This project involves optimizing Retrieval-Augmented Generation (RAG) pipelines to improve the accuracy and relevance of AI responses. This is a high-impact role that requires a self‑motivated professional who can hit the ground running and deliver results quickly.

Key Responsibilities & Deliverables

This role is focused on the successful completion of specific tasks and deliverables. Your responsibilities will include:

• Optimizing Retrieval-Augmented Generation (RAG) pipelines to improve the accuracy and relevance of AI responses.
• Implementing advanced "chunking" strategies and metadata tagging for enterprise documents.
• Selecting and fine‑tuning embedding models to better capture domain‑specific semantic meaning.
• Designing hybrid search systems combining vector similarity with traditional keyword search (BM25).
• Benchmarking various vector databases to ensure high retrieval speed and low memory overhead.

Required Skills & Experience

We are looking for someone with a proven track record of successful contract engagements. The ideal candidate will have:

• 3+ years of experience in Information Retrieval or AI Engineering.
• Deep expertise in Vector Databases (Pinecone, Chroma, Milvus) and LangChain/LlamaIndex. This isn't a learning role—you need to be a subject matter expert.
• Demonstrated ability to work autonomously and manage your own time effectively to meet project goals.
• Experience with Python, SQL, and natural language processing (NLP) techniques.
• Strong communication skills to provide clear and concise status updates to the project team.