•AI Application Development: Build and improve AI-powered applications using LLMs, foundation models, and modern AI APIs to solve real-world product problems.
•LLM Integration: Integrate commercial and open-source language models into applications, experimenting with prompting strategies, structured outputs, tool calling, and model selection.
•RAG & Knowledge Systems: Develop retrieval-augmented generation (RAG) pipelines to help AI systems retrieve and reason over relevant information from documents, databases, and other unstructured data sources.
•AI Agents & Workflows: Build and experiment with AI agents and multi-step workflows that use tools, access relevant context, and execute tasks with appropriate controls.
•Data Processing & Retrieval: Develop pipelines for document ingestion, text extraction, chunking, embeddings, metadata management, and semantic search.
•Evaluation & Reliability: Evaluate AI outputs for accuracy, relevance, consistency, and groundedness. Build test datasets and evaluation workflows to identify failure cases and improve system performance.
•Backend Integration: Collaborate with backend engineers to integrate AI capabilities into APIs and existing product workflows, ensuring maintainability and reliability.
•Performance & Cost Optimisation: Experiment with model selection, caching, retrieval strategies, and prompt optimisation to improve latency, reliability, and inference costs.
•Monitoring & Debugging: Investigate issues in AI pipelines, troubleshoot unexpected model behaviour, and help monitor the quality and performance of AI-powered features.
•Experimentation & Collaboration: Stay current with emerging AI techniques, evaluate relevant tools and frameworks, and work with product and engineering teams to translate customer problems into practical AI solutions.
WHAT YOU’LL BRING
•Experience: 1–3 years of experience in AI engineering, machine learning engineering, software engineering, or a related field. Strong internships and hands-on projects involving AI applications are also valuable.
•Programming: Proficiency in Python and the ability to write clean, modular, maintainable code.
•LLM Fundamentals: Understanding of how LLMs work at an application level, including prompting, context windows, token limits, structured outputs, and common failure modes.
•RAG & Embeddings: Familiarity with embeddings, vector search, document retrieval, and the fundamentals of building RAG-based applications.
•APIs & Backend Fundamentals: Experience working with REST APIs, JSON, asynchronous operations, and integrating external services into applications.
•Data Handling: Ability to work with structured and unstructured data, using tools such as Pandas and SQL where appropriate.
•Testing & Debugging: Ability to test application behaviour systematically, investigate failures, and improve the reliability of AI-driven workflows.
•Problem-Solving: Strong analytical thinking and the ability to break down ambiguous problems into practical engineering solutions.
•Collaboration: Clear communication skills, openness to feedback, and the ability to work effectively with engineers and product stakeholders.
EXPERIENCE WE’D BE PARTICULARLY EXCITED ABOUT
•Production LLM Applications: Experience building and deploying LLM-powered applications beyond simple prompt-based prototypes.
•Agentic Systems: Hands-on experimentation with AI agents, tool calling, function calling, or multi-step reasoning workflows.
•AI Frameworks: Familiarity with frameworks and libraries such as LangChain, LangGraph, LlamaIndex, or equivalent tools.
•Vector Databases & Search: Experience with vector databases or search technologies such as pgvector, Pinecone, Weaviate, or Elasticsearch.
•Evaluation & Observability: Exposure to LLM evaluation frameworks, tracing, monitoring, and tools for debugging AI workflows.
•Cloud & Deployment: Familiarity with Docker, cloud platforms, deployment workflows, or serving AI applications in production.
•Document Intelligence: Experience extracting information from PDFs, long-form documents, or complex business records.
•B2B SaaS & Enterprise AI: Experience building AI capabilities for business users, particularly where accuracy, data security, and reliability matter.
•AEC & Revenue Operations: Interest in applying AI to proposals, project information, client relationships, marketing, and revenue operations in the AEC industry.
