AI Engineer

Remarcable builds the software that keeps trade contractors' crews equipped and on schedule. Our platform brings material procurement, inventory and tool tracking, prefab requisitions and invoice automation into one place, connecting field crews, warehouses, purchasing teams and suppliers in real time. We were founded by former foremen, purchasers, project managers and engineers who lived the jobsite pain we now solve, and today we serve more than 40% of the top 50 electrical contractors in the U.S.

Backed by a $15M Series A led by Insight Partners (June 2025), we are growing fast across our teams in the U.S. and Vancouver, B.C. That growth is why we are hiring for this role.

As our AI Engineer, you will turn the data flowing through Remarcable every day (purchase orders, quotes, invoices, inventory and supplier catalogs) into AI features that save contractors real hours. You will design, build and ship LLM-powered capabilities into our product and internal tools, from intelligent invoice matching and document extraction to an AI assistant that answers questions on top of our new data lakehouse. You will report to the VP of Engineering and work side by side with our Product, Data and Full Stack Engineering teams.

This is a hands-on, builder role at a company where AI is still being defined. One week you may prototype a retrieval pipeline or an agent that drafts a purchase order; the next you may harden it for production, set up evaluations, or explain to a customer how it works. You will help set the patterns, tools and guardrails that every future AI feature at Remarcable is built on.

• Design, build and ship LLM-powered features across the platform, such as invoice and PO matching, quote and document extraction, catalog normalization and smart purchasing suggestions.

• Build retrieval-augmented generation (RAG) and agentic workflows that act on real procurement, inventory and accounting data.

• Own features end to end: prototype, production rollout, monitoring and iteration based on customer feedback.

• Partner with our Data Engineering team to use the data lakehouse as the foundation for AI features and an internal AI assistant.

• Build and maintain embedding pipelines, vector search and the APIs that serve AI features to our web and mobile apps.

• Integrate with leading model providers (e.g., Anthropic, OpenAI) and choose the right model for each job based on quality, latency and cost.

• Create evaluation sets and automated tests so we can measure accuracy before and after every change.

• Monitor AI features in production for accuracy, cost, latency and drift, and fix issues quickly.

• Put guardrails in place for data privacy, security and customer trust, supporting our ISO and compliance work.

• Work with Product Managers to find the workflows where AI saves contractors and suppliers the most time.

• Help Customer Success, Support and internal teams automate manual work with AI tools.

• Share what you learn through documentation, demos and code reviews, and raise the AI fluency of the whole engineering team.

• Lead or support projects such as AI-assisted AP automation, a natural-language assistant for our customers, and AI-powered supplier onboarding.

• Bring ideas, raise risks and pitch in wherever the team needs it.

• 4+ years of software engineering experience, including at least 1–2 years building and shipping LLM or machine learning features to production.

• Strong Python skills and experience building production APIs and services.

• Hands-on experience with LLM APIs, prompt design, RAG, embeddings and vector databases (e.g., pgvector, Pinecone, OpenSearch).

• Experience building evaluations and measuring model quality, not just demos.

• Solid SQL and data skills; comfortable working with messy, real-world business data.

• Experience with cloud infrastructure (AWS preferred), CI/CD and observability.

• Clear communication skills and the ability to explain AI trade-offs to non-technical teammates and customers.

• A builder's mindset: comfortable with ambiguity, quick to create structure, and happy to do both strategic and hands-on work.

• Experience with agent frameworks, tool use or Model Context Protocol (MCP) integrations.

• Document AI experience: OCR and extraction from invoices, quotes, packing slips or drawings.

• Familiarity with construction, trades, procurement, ERP or accounting systems.

• Experience with lakehouse or modern data stack tools (e.g., Databricks, Snowflake, dbt).

• Experience at a high-growth startup or scale-up, ideally in B2B SaaS.

• Experience fine-tuning or hosting open-source models.