Full Stack AI Engineer

What matters most

• Reliable LLM Workflow Engineering

  The role requires building robust AI systems using RAG, agents, and tool calling. You must prioritize implementing observability, guardrails, and fallbacks to ensure production-grade reliability, moving beyond simple prototyping to stable, maintainable AI solutions.

• Systematic AI Evaluation

  You are expected to build rigorous evaluation pipelines. This involves creating golden datasets and utilizing LLM-as-a-judge methods to measure performance, ensuring that model quality and latency remain within acceptable bounds for client applications.

• Inclusive Full-Stack Development

  Beyond AI, you must maintain high-quality full-stack applications using React.js and TypeScript. A critical priority is ensuring these applications meet WCAG 2.2 Level A and AA accessibility standards, requiring familiarity with assistive technologies.

Likely challenges to think through

• Balancing AI Performance and Cost

  You will face the inherent trade-off between model quality, latency, and operational cost. Prepare to discuss how you make architectural decisions—such as choosing between different model sizes or caching strategies—to meet client budget and performance requirements.

• Integrating Accessibility into Agile Cycles

  Maintaining WCAG compliance while working in a fast-paced agile environment can be difficult. Prepare to explain how you incorporate automated accessibility testing into CI/CD pipelines to prevent regressions without slowing down development velocity.

Topics to prepare

• LLM Evaluation Frameworks

  The role requires building evaluation pipelines using golden datasets and LLM-as-a-judge methods. Exercise: Design a small-scale evaluation suite for a RAG pipeline, defining metrics for accuracy and hallucination, and compare deterministic evaluators against LLM-based scoring to understand trade-offs in cost and reliability.

• Digital Accessibility Standards (WCAG 2.2)

  The company emphasizes inclusive technology compliant with WCAG 2.2 Level A and AA. Exercise: Review the WCAG 2.2 success criteria and perform a manual audit of a simple React component using a screen reader like NVDA or VoiceOver to identify common accessibility barriers.

• AI System Reliability and Observability

  The role involves implementing guardrails, fallbacks, and monitoring for AI systems. Exercise: Map out a monitoring strategy for an LLM application that includes tracking latency, token usage, and error rates, and define a fallback mechanism for when the primary model fails or returns invalid structured output.

Skills to revise

• Full-Stack Development with TypeScript

  The role requires building applications using React.js, TypeScript, and Node.js. Exercise: Build a small full-stack application that integrates a simple LLM API, ensuring type safety across the frontend and backend, and implement a basic CI/CD pipeline to automate testing and deployment.

• RAG and Tool Calling Implementation

  The role involves building LLM workflows with RAG and tool calling. Exercise: Develop a prototype that uses a vector database to retrieve context for a query and implements a tool-calling function that allows the LLM to interact with an external API or database.

• Infrastructure as Code and Containerization

  The role prefers knowledge of Docker and Infrastructure as Code. Exercise: Create a Dockerfile for a Node.js application and write a basic script or configuration file to deploy the containerized service, focusing on environment consistency and ease of maintenance.

• Design an LLM evaluation framework

  Prepare a technical summary or project walkthrough demonstrating how you build evaluation pipelines. Include your approach to using golden datasets, regression testing, and LLM-as-a-judge methods to ensure model quality and reliability.

• Demonstrate full-stack AI integration

  Prepare a code sample or architectural diagram showing a React/TypeScript/Node.js application that integrates an LLM workflow. Highlight how you implemented RAG, tool calling, or agentic patterns while maintaining low latency.

• Prepare for accessibility-focused technical discussion

  Review your experience with WCAG 2.2 Level AA standards. Be ready to explain how you have implemented inclusive design in previous projects and how you test for accessibility using tools like VoiceOver, NVDA, or TalkBack.

• Formulate questions on AI reliability and observability

  Prepare thoughtful questions for the interviewers regarding their current stack for monitoring AI guardrails, handling fallbacks, and managing CI/CD pipelines for AI-driven features.

• Review infrastructure and deployment practices

  Be prepared to discuss your experience with Docker, Infrastructure as Code, and Bash scripting. Explain how you have used these tools to manage deployments and ensure consistent, scalable environments for production applications.