Motion Recruitment
Overview
Fantastic opportunity with a growing EV auto manufacturer for a Staff LLM–RAG Engineer (Contract) to lead the development and optimization of enterprise-grade retrieval-augmented generation systems. This is a 100% remote contract opportunity.
Location: Remote (Central Time). Duration: 12+ months to start.
Responsibilities
Lead RAG Architecture Design – Define and implement best practices for retrieval-augmented generation systems, ensuring reliability, scalability, and low-latency performance.
Full-Stack AI Development – Build and optimize multi-stage pipelines using LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, or custom).
Programming & Integration – Develop services and APIs in Python and Golang to support AI workflows, document ingestion, and retrieval processes.
Search & Retrieval Optimization – Implement hybrid search, vector embeddings, and semantic ranking strategies to improve contextual accuracy.
Prompt Engineering – Design and iterate on few-shot, chain-of-thought, and tool-augmented prompts for domain-specific applications.
Mentorship & Collaboration – Partner with cross-functional teams and guide engineers on RAG and LLM best practices.
Performance Monitoring – Establish KPIs and evaluation metrics for RAG pipeline quality and model performance.
Ideal Background
8+ years in software engineering or applied AI/ML, with at least 2+ years focused on LLMs and retrieval systems.
Strong proficiency in Python and Golang or Rust, with experience building high-performance services and APIs.
Expertise in RAG frameworks (LangChain, LangGraph, LlamaIndex) and embedding models.
Hands-on experience with vector databases (Databricks Vector Store, Pinecone, Weaviate, Milvus, Chroma).
Seniority level
Mid-Senior level
Employment type
Contract
Job function
Information Technology
Industries
Information Services
#J-18808-Ljbffr
Location: Remote (Central Time). Duration: 12+ months to start.
Responsibilities
Lead RAG Architecture Design – Define and implement best practices for retrieval-augmented generation systems, ensuring reliability, scalability, and low-latency performance.
Full-Stack AI Development – Build and optimize multi-stage pipelines using LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, or custom).
Programming & Integration – Develop services and APIs in Python and Golang to support AI workflows, document ingestion, and retrieval processes.
Search & Retrieval Optimization – Implement hybrid search, vector embeddings, and semantic ranking strategies to improve contextual accuracy.
Prompt Engineering – Design and iterate on few-shot, chain-of-thought, and tool-augmented prompts for domain-specific applications.
Mentorship & Collaboration – Partner with cross-functional teams and guide engineers on RAG and LLM best practices.
Performance Monitoring – Establish KPIs and evaluation metrics for RAG pipeline quality and model performance.
Ideal Background
8+ years in software engineering or applied AI/ML, with at least 2+ years focused on LLMs and retrieval systems.
Strong proficiency in Python and Golang or Rust, with experience building high-performance services and APIs.
Expertise in RAG frameworks (LangChain, LangGraph, LlamaIndex) and embedding models.
Hands-on experience with vector databases (Databricks Vector Store, Pinecone, Weaviate, Milvus, Chroma).
Seniority level
Mid-Senior level
Employment type
Contract
Job function
Information Technology
Industries
Information Services
#J-18808-Ljbffr