9fin AI Engineer interview questions & guide 2026

The questions below represent the core technical and behavioral competencies we evaluate. Use these as a framework to assess your readiness across our primary engineering domains.

Generative AI and LLMs

These questions evaluate your deep understanding of modern language models and your ability to apply them to real-world data.

• How would you design a RAG pipeline to ensure high fidelity when extracting information from dense, long-form legal documents?
• What are the primary trade-offs when choosing between fine-tuning a smaller model versus using a larger model with in-context learning?

Preparation at 9fin requires a balance of theoretical knowledge and practical engineering pragmatism. You should focus on how your technical skills translate into business value within a financial technology context.

Technical Proficiency – We assess your depth in Python, machine learning frameworks, and modern AI tooling. You must be comfortable discussing the inner workings of current LLM architectures and the practicalities of deployment.

Architectural Thinking – You will be evaluated on your ability to design end-to-end systems. Strong candidates demonstrate a clear understanding of the trade-offs involved in system design for LLM serving, specifically regarding performance, cost, and maintainability.

Problem-Solving Agility – We value candidates who can navigate ambiguity. When presented with a complex scenario, structure your thinking, state your assumptions clearly, and discuss the limitations of your proposed solution.

Our interview process is designed to be rigorous but transparent, focusing on your ability to solve real-world problems. We prioritize candidates who exhibit both strong technical depth and a collaborative mindset, as our AI Engineers work closely with product and data teams to deliver high-impact features.

The process typically begins with a technical screening to establish a baseline in coding and AI concepts. This is followed by a deeper dive into system design and specialized AI domains, often involving a take-home assignment or a live coding/design session. We conclude with a behavioral round to assess how you function within our team culture.

This timeline provides a high-level view of your journey from initial contact to the final decision. Use this to pace your study, ensuring you allocate sufficient time to both hands-on coding practice and high-level architectural brainstorming.

RAG and Information Retrieval

This area is critical as it forms the backbone of our legal and financial extraction tools. We look for deep knowledge of retrieval strategies, re-ranking, and document indexing.

• Embeddings – Understanding how to select and fine-tune embedding models for specific domains.
• Vector Search – Experience with vector databases and performance tuning for large-scale retrieval.
• Evaluation – Methods for measuring retrieval precision and recall in a RAG pipeline.

As an AI Engineer at 9fin, you will lead the development of intelligent features that automate the extraction and analysis of financial documents. You will spend your time building and maintaining RAG pipelines, fine-tuning models to handle legal jargon, and ensuring that our AI systems are performant and reliable.

Collaboration is essential. You will work alongside software engineers to integrate your models into the production stack and with product managers to define the next generation of AI-driven features. You will be responsible for the entire lifecycle of your models, from experimentation and evaluation to deployment and monitoring in a production environment.

We seek engineers who combine a strong foundation in computer science with a passion for applied AI.

• Must-have skills:
  • Proficiency in Python and modern ML libraries (PyTorch, Hugging Face).
  • Practical experience with RAG and vector databases.
  • Solid understanding of LLM architecture and fine-tuning.
  • Experience in building and deploying scalable backend systems.
• Nice-to-have skills:
  • Experience with Knowledge Graphs.
  • Familiarity with financial or legal domains.
  • Background in building multi-agent or autonomous systems.

Q: How much time should I spend preparing for the coding rounds? A: Dedicate consistent time to practicing algorithmic problems, focusing on efficiency and clean code. Aim for a level of proficiency where you can solve medium-difficulty problems in under 30 minutes.

Q: Is there a specific focus on financial domain knowledge? A: While a background in finance is a bonus, it is not a strict requirement. We value your ability to learn the domain quickly and apply your AI expertise to solve specific financial problems.

Q: What is the team culture like for AI Engineers at 9fin? A: We are highly collaborative and engineering-led. You will have a high degree of autonomy and will be expected to contribute to both the technical strategy and the execution of our product roadmap.

• Structure your answers: Use the STAR method (Situation, Task, Action, Result) for behavioral questions to keep your responses focused and impactful.
• Think aloud: During coding and design sessions, verbalize your thought process. This helps interviewers understand your reasoning, even if you run into a roadblock.
• Focus on trade-offs: In system design, there is rarely one "correct" answer. Always discuss the pros and cons of your design decisions (e.g., latency vs. accuracy).
• Be prepared to discuss your past projects: Be ready to deep-dive into any project on your resume, especially regarding the challenges you faced and how you overcame them.

The AI Engineer position at 9fin is an exceptional opportunity to build technology that directly shapes the future of financial intelligence. By focusing on your core technical competencies, architectural thinking, and your ability to communicate complex ideas, you will be well-positioned to succeed in our interview process.

Candidates can explore additional interview insights, practice questions, and preparation resources on Dataford. We encourage you to use these resources to refine your approach and gain confidence before your interviews.

The provided compensation data reflects standard market ranges for this role, accounting for variations in total experience and specific technical expertise. Candidates should interpret these figures as a starting point for discussions, keeping in mind that total packages often include base salary, equity, and performance-based components.