AI Engineer

Das ist der Job

AI Engineer Porters is building the operating system for autonomous banking, and process accuracy is our moat.

Darum lohnt es sich

Technical Proficiency: strong proficiency in Python is mandatory, including experience with data manipulation libraries like Pandas and NumPy, and experience building evaluation pipelines using modern frameworks (Hugging Face, OpenAI SDKs, LangChain).

Gear & Culture: new MacBook, merch, regular team events and dinners, a fast‑paced, inclusive, and impact‑oriented culture. We are processing tens of thousands of documents per month, deriving instructions for banks to act on.

We are looking for an AI Systems Engineer to perpetually increase our AI accuracy, move ideas from the “lab” into production in our high-volume pipeline. Your goal is 99.99% accuracy on very complex document types.

Mission: lead the systematic R&D required to tune LLMs for high‑stakes document types (e.g., insolvency notices, seizure orders), ranging from improved prompting via rethinking our AI document extraction approach to fine‑tuning models. In short, you are building the engineering framework that allows us to deploy models successfully at scale.

This job is in person (Zurich, Switzerland) or remote (CET +/-3 hours). Your responsibilities Own the Evaluation Workbench: manage the full prompt evaluation lifecycle. Define test batches, manage model execution, and implement rigorous performance scoring to improve our current production engine.

Develop Robust Metrics: select and create custom quantitative metrics (Evals) tailored to banking benchmarks. Move beyond generic "accuracy" to granular measurements of extraction quality, hallucination rates, and specific field‑level precision.

Systematic Prompt Optimization (Closed‑Loop): conduct in‑depth analysis of evaluation results to identify gaps. Iteratively refine system prompts, few‑shot examples, and Chain‑of‑Thought (CoT) logic to maximize performance against our metrics. Pioneer Automation: design a closed‑loop feedback system.

Long‑term goal is to build the architecture that autonomously updates prompts or model parameters based on evaluation outcomes. Document Insights: clearly document evaluation methodologies and prompt versions, providing stakeholders with actionable data on the trade‑offs between cost, latency, and accuracy.

Your profile Academic Background: Bachelor’s, Master’s or PhD in Data Science, Computational Linguistics, Artificial Intelligence, Computer Science, or a closely related quantitative field. Evaluation & Metrics: demonstrated understanding of LLM evaluation techniques.

Know the difference between human‑in‑the‑loop vs. automated evals (LLM‑as‑a‑Judge) and can design metrics that objectively measure "quality." Prompt Engineering: deep understanding of prompt engineering principles (few‑shot, chain‑of‑thought) and how to apply them systematically rather than ad‑hoc.

Analytical Aptitude: proven ability to approach unstructured problems with a systematic, data‑driven methodology. Capable of translating complex evaluation results into concrete, code‑based refinements.

What We Offer Direct Impact: you will work directly with clients, ship fast, and see your code running in production at major financial institutions. Founder Collaboration: work closely with founders who have successfully built and scaled fintech and analytics products before.

Compensation: competitive salary and significant equity on top of your compensation. Growth: a dedicated development budget—free to use for whatever gives you a level‑up. Our stack LLM Infrastructure: AWS Bedrock (Anthropic Claude, Mistral, Gemini) and AWS ECS Fargate.

Data Science: Python (Pandas, NumPy) for high‑volume data manipulation and evaluation. Frameworks: LangChain, LlamaIndex, and custom agentic workflow pipelines. R&D Tooling: Agentic IDEs (Kiro, Cursor) for accelerated experimentation. #J-18808-Ljbffr