Exodigo is the leading underground mapping solution for non-intrusive discovery. Our platforms combine multi-sensor fusion, 3D imaging, and AI technologies to create complete, accurate underground maps that enable confident decision-making for customers across the built world. We transform the project lifecycle for our customers, who include key community stakeholders in the utilities, transportation and government sectors.
We are experiencing sky-rocketing growth and closed a historically large $96M Series B round in July of 2025.
Requirements:
- 5+ years of experience developing machine learning, AI, or software systems.
- B.Sc. in Computer Science, Computer Engineering, Electrical Engineering, or similar field.
- Experience in building and shipping meaningful, production-grade agentic systems - not just demos or prototypes. We’re looking for people who can walk us through what they built, the real problem it solved, and the value it created.
- Deep, in-depth understanding of LLMs - including how they work under the hood (transformer architecture, tokenization, attention, sampling), their failure modes, and the practical tradeoffs between models, prompting strategies, fine-tuning, and retrieval.
- Strong hands-on experience with modern agentic frameworks (e.g., LangGraph, LangChain, or equivalent) and with designing multi-step, tool-using workflows.
- Strong command of RAG, prompt engineering, evaluation methodologies, and techniques for improving reliability and reducing hallucinations in LLM-based systems.
- Strong proficiency in Python and experience with common AI/ML libraries and APIs (e.g., OpenAI, Anthropic, Hugging Face, PyTorch).
- solid software engineering practices, including writing clean, maintainable code and building robust, scalable systems.
Preferred Qualifications-
- M.Sc. or Ph.D. in a relevant field.
- Experience working with multimodal models that combine text with images, audio, or structured data.
- Familiarity with cloud environment development (AWS).
Job description:
Our Algorithms group is a highly multidisciplinary group at the core of our data processing and detection capabilities, integrating physics, signal processing, classical algorithms, and AI models to meet our unique requirements. As we scale, we are increasingly leveraging Large Language Models (LLMs) and agentic AI systems to accelerate our research, automate complex workflows, and unlock new capabilities across the company.
Job description
As an AI Engineer at Exodigo, you will join a fast-moving, highly technical team that works at the intersection of computer vision, deep learning, and modern AI. You will be the driving force behind our LLM and agentic AI efforts - exploring, evaluating, and deploying the latest research and tooling in this rapidly evolving space, and shaping how we apply it across the organization to turn cutting-edge ideas into production-ready tools and agents.
You will design and build LLM-powered applications, agentic workflows, and internal tools that empower our algorithms, mapping, and engineering teams to move faster and operate at greater scale. Collaboration is central to the role, you will work closely with researchers, engineers, and domain experts to identify high-impact opportunities, prototype quickly, and integrate **reliable AI systems into real-world products.
Key Responsibilities**
- Design, build, and iterate on LLM-powered applications, including retrieval-augmented generation (RAG) systems, agents, and fine-tuned models tailored to Exodigo's unique data and workflows.
- Develop agentic workflows that automate complex, multi-step tasks across research, data analysis, and engineering pipelines.
- Build internal tools and assistants that empower algorithm researchers, mapping experts, and engineers to work faster and more effectively.
- Evaluate and integrate the latest foundation models, frameworks, and techniques (e.g., prompt engineering, fine-tuning, tool use, multi-agent orchestration), keeping pace with rapid advances in the field.
- Design robust evaluation methodologies to measure quality, reliability, and safety of LLM-based systems.
- Collaborate closely with the Algorithms, Mapping, and Software teams to identify high-impact opportunities and transition prototypes into reliable, production-grade systems.
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