Remote AI Engineer: Build Scalable LLM Pipelines

Hire Hangar, Inc.; Argentina; Remote; ARS 181,906,000 - 272,860,000; Full time

Job summary

Hire Hangar, Inc. is seeking a hands-on AI Engineer to design, build, and deploy intelligent systems across client environments in a fully remote role. You will work end-to-end on the AI development lifecycle, from prototyping to production deployment and ongoing optimisation.

The ideal candidate thrives at the intersection of machine learning, software engineering, and product thinking, with fluency in modern AI tooling and systems thinking. Remote collaboration with US-based teams is essential.

Qualifications

• Strong programming skills in Python and software engineering fundamentals.
• Hands-on experience building with LLMs via API and SDK.
• Experience with RAG architectures and vector databases (Pinecone, Weaviate, Chroma).
• Familiarity with AI agent frameworks (LangChain, LlamaIndex, AutoGen, CrewAI).
• REST APIs and integrating third‑party services.
• Ability to work autonomously in a fast-paced remote environment.

Responsibilities

• Design and build AI-powered features, pipelines, and automation workflows.
• Integrate and fine-tune LLMs and embedding models into production apps.
• Develop and maintain RAG pipelines and vector search systems.
• Write clean code across backend and API layers for AI features.
• Evaluate and iterate model outputs for quality and reliability.
• Collaborate with cross-functional teams to scope AI solutions.
• Stay current with AI tooling and approaches.
• Document designs, system behavior, and deployment processes.

Skills

Python programming

LLM integration

RAG pipelines

Vector databases

REST APIs

LangChain

LLM tooling

Remote work experience

Cloud platforms

Docker

Tools

Pinecone

Weaviate

Chroma

LangChain

LlamaIndex

CrewAI

Job description

Hire Hangar, Inc. is seeking a hands-on AI Engineer to design, build, and deploy intelligent systems across client environments in a fully remote role. You will work end-to-end on the AI development lifecycle, from prototyping to production deployment and ongoing optimisation.

The ideal candidate thrives at the intersection of machine learning, software engineering, and product thinking, with fluency in modern AI tooling and systems thinking. Remote collaboration with US-based teams is essential.