Remote Agentic AI Engineer II

Industrious Ventures; Austin (TX); On-site; USD 140,000 - 210,000; Full time

Job summary

ICON is seeking an AI Software Engineer to design and ship AI-powered tools, workflows, and autonomous agents that accelerate ICON's construction technology platform. Remote work with periodic travel to ICON HQ in Austin, TX as projects require.

You will design and build agentic AI systems, develop LLM-powered features, architect multi-agent pipelines, and build robust RAG systems. You will own the full lifecycle of AI features and mentor others on best practices.

Qualifications

• 6+ years of professional software engineering experience.
• Experience shipping production-grade AI products using LLMs.
• Proficiency in TypeScript and/or Python.
• Experience with tool-use and agent orchestration.
• Ability to design structured evals for AI systems.
• Strong cross-functional communication and collaboration.
• Eligibility to obtain security clearance.

Responsibilities

• Design and build agentic AI systems automating complex workflows.
• Develop LLM-powered features using state-of-the-art models and APIs.
• Architect multi-agent pipelines and MCP integrations.
• Build robust RAG systems with document ingestion and embeddings.
• Collaborate with teams to identify AI automation opportunities.
• Own the full development lifecycle of AI features.
• Mentor others on agentic AI practices across the org.
• Stay at the leading edge of the agentic AI landscape.

Skills

LLM integration

Prompt engineering

Tool-use orchestration

Python/TypeScript

Cross-functional collaboration

Tools

LangChain

LlamaIndex

Claude Code

Job description

ICON is seeking an AI Software Engineer to design and ship AI-powered tools, workflows, and autonomous agents that accelerate ICON's construction technology platform. Remote work with periodic travel to ICON HQ in Austin, TX as projects require.

You will design and build agentic AI systems, develop LLM-powered features, architect multi-agent pipelines, and build robust RAG systems. You will own the full lifecycle of AI features and mentor others on best practices.