AI Engineer

The role

Responsibilities

• Design, build, and ship LLM-powered features such as invoice and purchase-order matching, document extraction, catalog normalization, and smart purchasing suggestions.
• Build RAG and agentic workflows over procurement, inventory, and accounting data.
• Own feature delivery from prototype through production rollout, monitoring, and customer-feedback iteration.
• Partner with Data Engineering on lakehouse-based AI features and an internal assistant.
• Build and maintain embedding pipelines, vector search, and APIs for web and mobile AI features.
• Integrate model providers such as Anthropic and OpenAI and select models based on quality, latency, and cost.
• Create evaluation sets and automated tests to measure accuracy across changes.
• Monitor production accuracy, cost, latency, and drift, and fix issues.
• Establish privacy, security, and customer-trust guardrails and support ISO and compliance work.
• Partner with Product to identify AI opportunities and help Customer Success, Support, and internal teams automate manual work.
• Share learning through documentation, demos, and code reviews and improve AI fluency across engineering.
• Lead or support projects including AP automation, customer natural-language assistance, and AI-powered supplier onboarding.

Required qualifications

• At least four years of software engineering experience, including one to two 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 such as pgvector, Pinecone, or OpenSearch.
• Experience building evaluations and measuring model quality, not just demos.
• Solid SQL and data skills; comfort working with messy business data.
• Cloud infrastructure, CI/CD, and observability experience; AWS is preferred.
• Clear communication and ability to explain AI trade-offs to non-technical teammates and customers.
• A builder’s mindset, comfort with ambiguity, and willingness to do strategic and hands-on work.

Preferred qualifications

• Experience with agent frameworks, tool use, or Model Context Protocol integrations.
• Document AI experience, including 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 such as Databricks, Snowflake, or dbt.
• Experience at a high-growth startup or B2B SaaS scale-up.
• Experience fine-tuning or hosting open-source models.

Why join

• Be an early dedicated AI engineer shaping how AI is built at the company.
• Work on operational problems using real data and see features used in the field, warehouses, and purchasing offices.
• Collaborate with company leaders on meaningful initiatives and grow as the company scales.
• The company offers a competitive salary, performance-based bonus, health and wellness benefits, and a collaborative culture.