Applied AI Engineer

You will work across the core systems that power our product: document intelligence, retrieval, agentic workflows, and the infrastructure required to deploy them reliably in production. This role is well-suited for someone who likes operating across layers, from messy PDF parsing problems to LLM workflow design, evals, and production deployment.

Responsibilities:

• Document Intelligence: Build and improve the pipelines that turn complex financial documents into structured, usable data.
• Retrieval and Agentic Workflows: Design and improve RAG systems, retrieval pipelines, and multi-step LLM workflows that extract, validate, reason over, and populate information into downstream outputs.
• Evaluation and Reliability: Build the infrastructure to measure system quality. Define evals, failure taxonomies, and operational metrics that help us understand where workflows break.
• Product and Infrastructure: Deploy scalable, resilient systems in production. Work closely with design, product, and business teams to rapidly build and iterate on user-facing workflows.

Requirements:

• Strong programming ability in Python and TypeScript.
• Experience integrating LLMs into production systems, including prompting, context management, structured outputs, and cost-performance tradeoffs.
• Experience building or working with document processing systems such as VLMs for OCR and layout parsing models.
• Comfort with cloud deployment and production systems, including containers, CI/CD, and Azure or GCP.
• Experience thinking carefully about system quality, including evaluation, observability, or failure analysis for complex AI workflows.

Good to Have:

• Experience with RAG systems, hybrid retrieval, reranking, and eval set design.
• Experience with vision-language models or multimodal document understanding.
• Familiarity with Azure or GCP-based AI infrastructure.
• Experience building multi-step agentic systems or using modern agent tooling.
• Genuine curiosity about financial services workflows, how investment banking, private equity, equity research, credit, or diligence actually work day to day.

Nice to Have:

• You are AI-native. Tools like Claude Code, Cursor, Codex, and modern model APIs are part of your everyday workflow.
• You know where they are powerful and where they fail, and you build with judgment around them.
• You are an owner. Autonomous, self-directed, and comfortable with ambiguity. You take responsibility for outcomes, not just tasks.
• You are energised by difficult problems, things that are technically hard, operationally messy, and valuable when solved well.
• You want to understand how your users actually work. What makes a workflow painful, what accuracy really means in context, and why a product decision matters.