Job Summary

You will build the AI features that make those products work and you will build them for production, not for a demo. That means retrieval systems over large document corpora that arrive as inconsistent PDFs, scans and HTML. Extraction pipelines that pull structured fields out of documents that were never designed to be parsed. Classification against real-world taxonomies. Agentic workflows where they genuinely beat a deterministic pipeline. And, underneath all of it, the evaluation harnesses that tell you whether any of it actually works.

Because you will work across several ventures rather than one, you will also be one of the people deciding what gets built once and reused everywhere. That leverage is the most interesting part of the job.

This is an engineering role. Most of your time goes into services, pipelines, data plumbing, tests and evals. The model is a component, not the job.

Responsibilities

- Take AI features from vague problem statement through to specified, built, evaluated and deployed.
- Build RAG systems end to end ingestion, parsing, chunking, embedding, retrieval, reranking, grounding and citation over messy documents at scale.
- Build extraction and classification pipelines that map unstructured documents onto structured taxonomies, and handle the long tail where documents dont cooperate.
- Build evaluation harnesses, golden datasets and regression suites, and feed real production failures back into them.
- Own cost and latency as first-class constraints: model routing, caching, batching, context economy, fallbacks when a provider degrades.
- Ship with real observability tracing, quality dashboards, spend tracking.
- Identify whats common across ventures and build it once, properly, with documentation.

What we're looking for

- 2-4 years of professional software engineering, including at least a year building with language models. Strong engineering fundamentals matter more here than exotic ML knowledge.




- Youve shipped an LLM-powered feature to real users in production and can describe it end to end-the data flow, the failure handling, what it cost, what broke after launch, and what you changed. Notebooks, demos and course projects dont count for this one.
- Evaluation discipline: you can explain how you knew a system was good enough to ship, and how you measured whether changes made it better. The dataset, the metric, the baseline.
- Strong Python, plus real production practice: version control, testing, CI, containers, and operating a service you wrote. Comfort with SQL and a cloud platform.
- Practical command of the applied LLM toolkit: frontier model APIs, structured output, tool calling, embeddings and vector search, reranking, context management. We care that you understand the mechanics, not that youve used a particular framework.
- Judgment about when not to use a model: you can explain why you replaced an LLM call with a regex, a database query or a smaller model and why that was right.
- Clear written and spoken English.

Not required

- A PhD or publications were not building novel models.
- Experience training large models from scratch.
- A specific degree. We care about what youve shipped.

Why join

- Real production AI, at volume, on problems that dont have textbook answers.
- Breadth: youll work across several ventures rather than one narrow surface, and what you build gets reused.
- A domain with genuinely hard document problems: procurement data is as messy as it gets.
- Direct access to engineering leadership, short feedback loops, freedom in technical choices.
- The field moves quarterly and youre expected to keep up on company time evaluating whats current is part of the job, not something you do at midnight.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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