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AI Engineer - Labrynth

Key Facts

Remote From: 
Full time
Mid-level (2-5 years)
English

Other Skills

  • •
    Communication
  • •
    Collaboration
  • •
    Proactivity
  • •
    Resourcefulness

Roles & Responsibilities

  • US citizen or permanent resident, physically located in the United States
  • 3-5 years of software engineering experience with solid Python skills
  • Experience with or strong interest in AI/LLM applications
  • Comfort working with cloud services, preferably GCP

Requirements:

  • Build and extend multi-agent AI pipelines for regulatory document analysis
  • Develop and improve GraphRAG-based knowledge retrieval systems using Neo4j
  • Integrate and orchestrate LLM calls for document processing workflows
  • Collaborate with engineers and directly with clients to understand domain requirements

Job description

About Labrynth

Labrynth accelerates progress by streamlining regulatory complexity. We build AI-powered platforms that navigate complex regulations, generate audit-level documentation, and provide certainty -- not shortcuts. Our technology serves clients across heavily regulated industries including energy, compliance, and government permitting.

We operate as a forward-deployed engineering organization: small, high-velocity teams embedded directly with clients to rapidly discover needs and ship production-quality solutions.

About the Role

We are looking for an AI Engineer to join our forward-deployed team supporting clients in regulated industries. This role requires a US person physically based in the United States due to export control regulations governing certain client materials.

You will work closely with senior engineering to build and maintain AI-powered regulatory document processing and pre-drafting systems. You'll receive guidance while taking ownership of meaningful features and deliverables. You'll also have direct client exposure and the opportunity to grow rapidly in a high-impact environment.

What You'll Do

  • Build and extend multi-agent AI pipelines for regulatory document analysis, gap identification, and pre-draft generation

  • Work with export-controlled materials in a compliant environment

  • Develop and improve GraphRAG-based knowledge retrieval systems using Neo4j

  • Integrate and orchestrate LLM calls for document processing workflows

  • Collaborate with engineers and directly with clients to understand domain requirements and translate them into technical solutions

  • Contribute to platform architecture as we modularize and scale our systems across multiple regulatory domains

What We're Looking For

  • US citizen or permanent resident, physically located in the United States (non-negotiable)

  • 3-5 years of software engineering experience with solid Python skills

  • Experience with or strong interest in AI/LLM applications (agent pipelines, RAG, document processing)

  • Comfort working with cloud services, preferably GCP (Cloud Run, BigQuery, Memorystore)

  • Familiarity with relational or graph databases (Neo4j experience is a plus)

  • Experience with Django or FastAPI

  • Proactive and resourceful -- comfortable taking ownership of tasks while collaborating closely with team members

  • Good communication skills for participating in client interactions alongside leadership

Nice to Have

  • Experience in regulated industries (energy, defense, healthcare, finance)

  • Familiarity with energy or government regulatory frameworks

  • DOE Q-clearance or ability to obtain one

  • Experience with document processing pipelines (Scrapping, OCR, PDF extraction)

  • Contributions to open-source AI/ML projects

  • Experience with task queues and distributed processing

What We Offer

  • High-impact work at the intersection of AI and critical regulatory space

  • Small team with outsized influence -- your work directly shapes the product

  • Modern AI-native development environment

  • Accelerated growth path

  • Remote-first within the US

Values We Hire For

  • Character -- integrity and trustworthiness above all

  • Competency -- evoking trust and reliably delivering

  • Togetherness -- family-level support and alignment

  • Impact -- meaningful outcomes over activity

  • Commitment -- ownership and follow-through

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