Remote Python AI Engineer - RAG, LLMs & Graph DBs

SPACE AI; Delray Beach (FL); On-site; USD 120,000 - 190,000; Full time

Benefits offered by this job

Flexible working hours

Remote work options

Performance bonus

Paid sick time

Paid time off

Provident Fund

Job summary

SPACE AI is seeking an AI/ML engineer to design and optimize end-to-end RAG pipelines and efficient LLM inference. You will work with Neo4j graphs, vector databases, and microservices to deliver scalable AI solutions.

The role emphasizes hands-on ML engineering, collaboration across teams, and delivering low-latency AI systems in production environments. Remote-friendly with flexible hours and growth opportunities.

Qualifications

• Proficiency in Python and ML libraries as needed for building AI systems.
• Experience building RAG pipelines and integration with LangChain or LlamaIndex.
• Hands-on with graph databases, especially Neo4j and Cypher.
• Experience with vector databases (FAISS, Pinecone) and embedding techniques.

Responsibilities

• Build end-to-end RAG pipelines for context-aware AI responses.
• Implement and fine-tune vLLM for efficient LLM inference.
• Collaborate with ML engineers to deploy transformer models and vector DBs.
• Architect and optimize Neo4j graphs to model project knowledge networks.
• Develop Python microservices (FastAPI/Flask) for data flows and APIs.
• Monitor performance, run A/B tests, ensure low-latency production.
• Ensure scalability and efficiency of AI systems.

Skills

Python programming

RAG pipelines

Strong problem solving

Team collaboration

Education

Bachelor's degree in CS or related field

Tools

PyTorch

TensorFlow

Hugging Face Transformers

LangChain

LlamaIndex

Neo4j

Cypher

FAISS

Pinecone

AWS

GCP

Azure

Docker

Kubernetes

FastAPI

Flask

MLflow

Kubeflow

Job description

SPACE AI is seeking an AI/ML engineer to design and optimize end-to-end RAG pipelines and efficient LLM inference. You will work with Neo4j graphs, vector databases, and microservices to deliver scalable AI solutions.

The role emphasizes hands-on ML engineering, collaboration across teams, and delivering low-latency AI systems in production environments. Remote-friendly with flexible hours and growth opportunities.