AI & LLMs Python Engineer - RAG, Graph DBs (Remote)

SPACE AI; Delray Beach (FL); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Competitive salary

Remote work options

Professional development

Job summary

SPACE AI is seeking an AI/ML engineer to build end-to-end RAG pipelines for context-aware AI responses and to optimize large language model inference with vLLM. You will collaborate with ML engineers to deploy transformer models and work with graph databases like Neo4j.

The role emphasizes scalable data architectures, Python-based microservices, and cloud/containerization skills to enable low-latency AI systems in production.

Qualifications

• Proficiency in Python and AI/ML libraries (PyTorch, TensorFlow, Hugging Face Transformers).
• Hands-on experience with graph databases, especially Neo4j (Cypher queries, graph algorithms).
• Demonstrated work on RAG pipelines (retrieval, reranking, generation) using LangChain or LlamaIndex.
• Experience with vLLM or similar LLM optimization tools (quantization, distributed inference).
• Knowledge of vector databases (e.g., FAISS, Pinecone) and embedding techniques.
• Familiarity with cloud platforms (AWS/GCP/Azure) and containerization (Docker, Kubernetes).

Responsibilities

• Build end-to-end RAG (Retrieval-Augmented Generation) pipelines for context-aware AI responses.
• Implement and fine-tune vLLM for efficient inference of large language models (LLMs).
• Collaborate with ML engineers to deploy transformer models and vector databases.

Skills

Python

ML libraries

RAG pipelines

vLLM

Graph databases

Neo4j

APIs

Docker/Kubernetes

Cloud platforms

MLflow

Tools

LangChain

LlamaIndex

PyTorch

TensorFlow

Transformers

FAISS

Pinecone

FastAPI

Flask

Neo4j Cypher

Job description

SPACE AI is seeking an AI/ML engineer to build end-to-end RAG pipelines for context-aware AI responses and to optimize large language model inference with vLLM. You will collaborate with ML engineers to deploy transformer models and work with graph databases like Neo4j.

The role emphasizes scalable data architectures, Python-based microservices, and cloud/containerization skills to enable low-latency AI systems in production.