We are looking for a Senior AI Engineer to help evolve our existing AI platform for steel sourcing and quotation workflows.
Our platform processes emails and documents, extracts structured product specifications, and uses AI agents to match customer requirements with supplier offers and available inventory. You’ll improve these capabilities by combining steel domain knowledge, retrieval, and reasoning to produce accurate, explainable results.
This is a hands-on senior role with technical leadership responsibilities. You’ll write code, investigate production issues, and deliver improvements while helping shape our architecture and engineering practices.
What you’ll do
Own and improve AI pipelines that extract and validate information from customer requests, supplier inventories, emails, PDFs, spreadsheets, and images.
Build agentic matching workflows that interpret customer requirements, retrieve candidate products, compare specifications and constraints, and rank suitable offers - with clear explanations and human review where needed.
Design and improve agent harnesses, including context management, tool access, workflow state, retries, stopping conditions, and human review.
Build and operate Model Context Protocol (MCP) servers and clients that connect agents with tools, business systems, and domain knowledge, with appropriate authentication, permissions, monitoring, and error handling.
Strengthen retrieval so agents interpret steel terminology and specifications correctly.
Evaluate and integrate commercial and open-source models based on accuracy, latency, cost, and deployment requirements.
Use Langfuse to manage prompts, trace agent behavior, and run evaluations that measure extraction and matching quality.
Turn production failures and user corrections into evaluation cases and improvements to prompts, tools, and execution logic.
Extend our Python services and integrate AI capabilities into existing product workflows.
Work with business colleagues to prioritize problems, guide technical decisions, and mentor engineers while remaining closely involved in implementation.
Our technology stack
AI and model infrastructure: Pydantic AI / LangGraph for agent orchestration, LiteLLM as the model gateway, and a mix of commercial and open-source models, including integrations with OpenAI, Anthropic, and Google.
Prompt management and evaluation: Langfuse for prompt versioning, tracing, evaluation datasets, and running evaluations.
Data and retrieval: PostgreSQL, pgvector, embeddings, hybrid search, and a steel domain knowledge base.
Backend: Python, FastAPI, Pydantic, async SQLAlchemy, and Alembic.
Application monitoring: Logfire and Sentry.
Cloud and delivery: Google Cloud, Kubernetes, Docker, Cloud Storage, Pub/Sub, Cloud Tasks, and GitHub Actions.
Product application: React, TypeScript, TanStack Query and Router, Tailwind CSS, and Hasura GraphQL.
Your primary focus will be the AI and Python backend, including agent harnesses and MCP integrations. You’ll collaborate with engineers across the wider platform.