At Front, customer communication is at the core of the business. An AI Engineer—and specifically the AI Engineering Manager for the Core AI Platform—plays a pivotal role in redefining how teams manage, triage, and respond to high-volume customer interactions. By building intelligent, highly reliable, and scalable backend systems, you will empower organizations to automate complex workflows, generate context-aware draft responses, and extract actionable insights from unstructured communication data.

This role is not about building isolated prototypes or simple API wrappers. You will be responsible for architecting and scaling the enterprise-grade Core AI Platform that integrates deeply with Front's collaborative inbox. The challenges you will tackle include real-time retrieval-augmented generation (RAG) over massive customer knowledge bases, low-latency processing of multi-channel communication threads, and ensuring strict data privacy and security compliance. Your work will directly impact millions of daily active users who rely on Front to keep their customer operations running seamlessly.

To succeed in this position, you must combine strong software engineering fundamentals with a pragmatic approach to artificial intelligence. Front values engineers who prioritize user impact, system reliability, and cost-efficiency. Whether optimizing LLM token usage, designing fallback mechanisms for external API dependencies, or leading a team of high-performing platform engineers, your contributions will shape the future of collaborative customer service.

The interview process at Front is designed to evaluate both your practical engineering capabilities and your strategic thinking. The questions outlined below are representative of the patterns and challenges reported by real candidates who have interviewed for engineering and AI-focused roles at the company.

AI System Design & Architecture

These questions evaluate your ability to design robust, production-ready AI systems that integrate seamlessly with enterprise application backends. Interviewers will look for your understanding of latency, cost, and reliability trade-offs.

• How would you design a real-time email summarization pipeline that handles sudden spikes in traffic without degrading user experience?
• Describe how you would build a retrieval-augmented generation (RAG) system for a customer support platform with multi-tenant data isolation.

Preparing for an interview at Front requires a balanced focus on platform engineering fundamentals and modern AI orchestration techniques. You should approach your preparation with a production-first mindset, showing that you understand how to move models from experimental notebooks into stable, customer-facing features.

AI Platform Engineering – You must demonstrate a deep understanding of how to build and maintain the infrastructure supporting AI models. This includes vector database management, embedding pipelines, semantic search optimization, and building resilient API wrappers around LLMs.

Pragmatic Problem-Solving – Front values engineers who choose the right tool for the job. Be prepared to explain when to use simple heuristics or traditional machine learning versus when to leverage expensive large language models, always keeping cost and latency in mind.

System Reliability & Monitoring – You will be evaluated on your ability to monitor AI systems in production. Be ready to discuss how you track model drift, handle output non-determinism, implement fallbacks, and design robust evaluation frameworks like LLM-as-a-judge.

Collaboration & Communication – As an engineer or manager on the Core AI Platform team, you must translate complex AI concepts into clear, actionable roadmaps for product and business partners. Strong communication and a collaborative mindset are essential.

The interview loop for the AI Engineer position at Front is highly structured, aiming to assess both your technical alignment and your cultural fit. While the process is rigorous, it is designed to move efficiently once initial contact is established.

Typically, the journey begins with a recruiter screen where you will discuss your background, your experience with AI systems, and your alignment with Front's mission. Following a successful initial screen, candidates transition into technical assessments that focus heavily on system design, practical coding, and platform architecture. The final stage is a comprehensive virtual onsite loop that deepens the technical evaluation and introduces behavioral and leadership discussions with key engineering stakeholders.

The timeline above illustrates the standard progression from your first conversation to the final hiring decision. Candidates should use this roadmap to pace their preparation, ensuring they dedicate sufficient time to both technical system design and behavioral storytelling. While team-specific requirements may occasionally introduce minor variations, the core focus on practical engineering and collaborative values remains consistent.

To succeed at Front, you must demonstrate deep expertise across several core technical domains. The engineering team looks for candidates who can bridge the gap between cutting-edge AI capabilities and stable enterprise software.

LLM Orchestration & RAG Pipelines

Building features on top of foundation models requires sophisticated orchestration to ensure accuracy, safety, and speed. You must show that you can build reliable pipelines that inject the right context into prompts at the right time.

Be ready to go over:

• Vector Databases & Indexing – Choosing and optimizing vector stores for fast, contextually relevant similarity searches.
• Context Window Management – Strategies for truncating, summarizing, and ranking retrieved data to fit within token limits while minimizing costs.
• Output Structuring – Utilizing tool calling, function calling, and schema validation libraries to guarantee parser-friendly model outputs.
• Advanced concepts (less common) – Fine-tuning embedding models for domain-specific vocabularies and implementing agentic multi-step reasoning loops.

Example questions or scenarios:

• "How would you design a system that dynamically decides whether to query a vector database, an internal SQL database, or a web search API to answer a user's query?"
• "Describe your approach to validating that an LLM's generated response strictly adheres to a predefined JSON schema in a production environment."

Platform Scalability & API Management

Since Front's AI capabilities are built on top of external foundation models, managing the infrastructure that interfaces with these providers is critical to maintaining high application performance.

Be ready to go over:

• Rate Limiting & Backoff – Implementing token-bucket algorithms and exponential backoff to handle provider-side rate limits.
• Fallback & Redundancy – Designing multi-provider failover strategies (e.g., falling back from OpenAI to Anthropic or self-hosted models) to guarantee uptime.
• Caching Architectures – Utilizing semantic and exact-match caching to prevent redundant model calls and reduce operational expenses.

Example questions or scenarios:

• "If our primary LLM provider experiences a 50% increase in latency, how does your platform architecture automatically detect this and reroute traffic without dropping user requests?"
• "Design a billing and usage tracking system that attribute LLM token costs to specific enterprise customers in real time."

AI Product Evaluation & Monitoring

Unlike traditional software, AI systems are non-deterministic. Front evaluates your ability to measure, monitor, and continuously improve the quality of AI-generated features.

Be ready to go over:

• Evaluation Frameworks – Setting up automated evaluation pipelines using synthetic data, ground-truth datasets, and LLM-as-a-judge patterns.
• Observability – Logging prompts, completions, latency, and user feedback (e.g., thumbs up/down) to pinpoint failure modes.
• Guardrails & Safety – Implementing input moderation and output validation layers to prevent toxic content, hallucinations, or data leaks.

Example questions or scenarios:

• "How would you set up an offline evaluation pipeline to test whether a new prompt template performs better than the current production prompt?"
• "What metrics would you track to identify if a deployed customer support agent is starting to hallucinate or provide outdated information?"

As an AI Engineer or AI Engineering Manager on the Core AI Platform team, your day-to-day work will directly shape how Front leverages artificial intelligence across its entire product suite. You will be responsible for the following key areas:

• Architecting the Core AI Platform – Design, build, and maintain the highly scalable, low-latency infrastructure, APIs, and microservices that power all AI features across Front.
• Collaborating with Product Teams – Partner closely with product managers, frontend engineers, and product designers to integrate AI capabilities seamlessly into the user interface.
• Optimizing Performance and Cost – Continuously monitor, benchmark, and optimize token usage, model latency, and API costs to ensure sustainable scaling of AI features.
• Establishing Engineering Standards – Set the benchmarks for prompt engineering, model evaluation, semantic search strategies, and code quality across the broader engineering organization.
• Mentoring and Technical Leadership – Guide other engineers on modern AI engineering best practices, conduct thorough code reviews, and foster a culture of pragmatic, high-impact technical execution.