What is an AI Engineer at Aircall?

As an AI Engineer (specifically focusing on AI Productivity) at Aircall, you are at the forefront of transforming voice communications into intelligent, actionable insights. Aircall is a leading cloud-based voice platform designed for sales and support teams. Your role is critical because you build the intelligence layer that sits on top of millions of daily conversations, turning raw audio data into productivity-enhancing features like automated call summaries, sentiment analysis, and real-time agent copilots.

Your impact directly influences how our customers interact with their clients. By integrating cutting-edge Large Language Models (LLMs) and machine learning pipelines into our core telephony product, you reduce friction for support agents and empower sales teams to close deals faster. You will also build internal productivity tools that streamline operations for our own engineering and go-to-market teams, acting as a force multiplier across the organization.

This role is unique because it combines the massive scale of real-time voice data with the fast-paced innovation of generative AI. You will not just be training models in isolation; you will be shipping production-ready AI features that solve immediate user problems. Expect to navigate the complexities of low-latency AI inference, data privacy constraints, and highly scalable system architectures.

Common Interview Questions

The following questions are highly representative of what candidates face during the Aircall interview loop. While you may not get these exact prompts, they illustrate the core patterns and technical depth we expect. Use these to guide your practice sessions.

Applied AI & LLM Engineering

This category tests your practical ability to build and optimize AI features using modern language models and frameworks.

• How do you evaluate the quality and accuracy of an LLM's output in a production environment?
• Explain the architectural differences between fine-tuning a model and using Retrieval-Augmented Generation (RAG). When would you choose one over the other?
• How would you design a prompt strategy to extract highly specific, structured JSON data from a messy, unstructured call transcript?
• Describe a situation where an AI feature you built suffered from high latency. How did you diagnose and resolve the bottleneck?
• What techniques do you use to prevent prompt injection or handle sensitive PII data before sending it to a third-party LLM API?

System Design & Architecture

These questions evaluate your ability to design scalable, resilient backends that can handle heavy AI workloads and high traffic.

• Design a system that ingests live audio streams, transcribes them in near real-time, and provides live AI-generated hints to a support agent.
• How do you handle asynchronous task processing for long-running AI jobs, ensuring the system remains responsive?
• Draw the architecture for a semantic search engine that allows users to query millions of past customer interactions.
• If our third-party LLM provider goes down, how would you design our system to fail gracefully and minimize user disruption?
• Explain how you would monitor and manage the API costs associated with serving an AI feature to thousands of active users.

Backend Coding & Problem Solving

This section focuses on your core software engineering skills, particularly in Python, data manipulation, and algorithmic thinking.

• Write a Python script using asyncio to concurrently fetch data from multiple third-party APIs and aggregate the results.
• Implement a rate-limiting middleware for an API endpoint to ensure users do not exceed their daily AI usage quota.
• Given a massive text file representing a call log, write an efficient function to parse, chunk, and prepare the text for vector embedding.
• How would you design a caching layer to store and retrieve frequent LLM responses to reduce API costs and latency?

Behavioral & Product Sense

We want to see how you collaborate, prioritize, and focus on delivering tangible value to the end user.

• Tell me about a time you built an internal tool that significantly improved your team's productivity. How did you measure its success?
• Describe a situation where you had to push back on a product manager's request because the AI technology was not capable of delivering the desired result.
• How do you stay up-to-date with the rapidly evolving AI landscape, and how do you decide which new tools are worth integrating into your stack?
• Tell me about a project that failed or did not meet expectations. What did you learn, and how did you adapt?

Getting Ready for Your Interviews

To succeed in our interview process, you need to approach your preparation with a balance of deep technical rigor and strong product sense. We do not just look for candidates who understand AI theory; we look for engineers who can deploy AI to solve real business challenges.

Applied AI & Engineering Mastery – This evaluates your ability to bridge the gap between AI models and software engineering. We look for strong proficiency in Python, experience with LLM frameworks, and a deep understanding of how to build robust, scalable APIs around AI endpoints. You can demonstrate strength here by discussing real-world trade-offs you have made regarding latency, cost, and model accuracy.

System Design & Architecture – This assesses your capability to design systems that handle Aircall's massive call volume. Interviewers will evaluate how you structure data pipelines, manage asynchronous tasks, and ensure high availability. Show your strength by designing systems that are resilient to failure and capable of processing audio and text data efficiently at scale.

Productivity & Product Sense – This measures your intuition for user experience and business value. Because you are building productivity tools, you need to understand the end-user's workflow. You can stand out by showing how you measure the success of an AI feature beyond technical metrics, focusing instead on user adoption and time saved.

Culture Fit & Ownership – This evaluates how you collaborate, handle ambiguity, and take ownership of your projects. Aircall values autonomy and cross-functional teamwork. Demonstrate this by sharing examples of how you have driven projects from ideation to deployment while collaborating with product managers and other engineering teams.

Interview Process Overview

The interview journey for an AI Engineer at Aircall is designed to be rigorous, transparent, and highly collaborative. You will begin with a recruiter screen to align on your background, expectations, and the specific focus of the AI Productivity role. From there, you will move into a technical screening phase, which typically involves a mix of coding fundamentals and applied AI problem-solving to ensure you have the baseline engineering chops required for our stack.

If successful, you will advance to the onsite interview loop, which is currently conducted virtually. This stage dives deep into your technical depth and behavioral alignment. You will face specialized rounds focusing on system design for AI products, deep dives into your past projects, and cross-functional collaboration. We emphasize practical, real-world scenarios over academic puzzles. We want to see how you think on your feet, how you handle constraints like latency and privacy, and how you communicate complex AI concepts to non-technical stakeholders.

What makes our process distinctive is our focus on shipping velocity and product impact. We care less about your ability to implement a neural network from scratch and more about your ability to leverage modern AI tools (like RAG architectures and commercial APIs) to deliver immediate value to Aircall users.

The visual timeline above outlines the typical progression of your interview stages, from the initial screen to the final executive or values alignment round. Use this map to pace your preparation, ensuring you dedicate ample time to both hands-on coding practice and high-level system design before you reach the final onsite stages.

Deep Dive into Evaluation Areas

Applied AI and LLM Integration

This area matters because the core of your role involves leveraging modern AI to build user-facing features. We evaluate your practical experience with Large Language Models, prompt engineering, and context injection techniques. Strong performance means showing a nuanced understanding of when to use a simple prompt, when to implement Retrieval-Augmented Generation (RAG), and when to fine-tune a model.

Be ready to go over:

• Prompt Engineering & Optimization – Structuring prompts for consistent, parsable outputs (e.g., JSON) and handling edge cases in user inputs.
• RAG Architectures – Designing vector search pipelines, chunking strategies for long transcripts, and managing context windows.
• Cost & Latency Management – Balancing the trade-offs between using high-powered models (like GPT-4) versus faster, cheaper, or open-source alternatives.
• Advanced concepts (less common) – Multi-agent systems, semantic caching, and handling hallucinations in highly sensitive business contexts.

Example questions or scenarios:

• "How would you design an AI feature that automatically summarizes a 45-minute sales call and extracts action items with high accuracy?"
• "Walk me through how you would optimize an LLM pipeline that is currently too slow for real-time agent assistance."
• "Describe a time you had to mitigate AI hallucinations in a production environment. What was your approach?"

Backend Engineering and API Design

AI models are only as good as the infrastructure supporting them. This area evaluates your ability to wrap AI capabilities into robust, scalable software. We look for deep expertise in Python, asynchronous programming, and RESTful API design. A strong candidate writes clean, maintainable code and understands how to integrate AI services into a larger microservices architecture.

Be ready to go over:

• Python Fundamentals – Proficiency in modern Python, including typing, generators, and asynchronous frameworks (like FastAPI or Asyncio).
• API Development – Designing idempotent endpoints, handling rate limits from external AI providers, and managing webhooks.
• Data Handling – Efficiently processing large text payloads, managing database transactions, and ensuring data privacy and compliance.
• Advanced concepts (less common) – Streaming responses (Server-Sent Events) for real-time AI typing effects, and optimizing CPU/memory usage for local model inference.

Example questions or scenarios:

• "Design a robust API endpoint that accepts a large audio file, transcribes it asynchronously, and notifies the client when the AI summary is ready."
• "How do you handle rate-limiting and retries when depending on third-party AI APIs like OpenAI or Anthropic?"
• "Write a Python function to efficiently parse and clean a massive, poorly formatted JSON response from an LLM."

System Design for AI Products

Because Aircall handles millions of calls, your AI solutions must scale flawlessly. This area tests your ability to design distributed systems that incorporate AI workloads. We evaluate how you handle bottlenecks, data storage, and asynchronous processing. Strong performance involves drawing clear architecture diagrams, identifying single points of failure, and justifying your technology choices.

Be ready to go over:

• Asynchronous Processing – Using message brokers (like Kafka or RabbitMQ) to decouple heavy AI inference tasks from the main application thread.
• Database & Storage Selection – Choosing the right datastores for vector embeddings, relational metadata, and raw transcript logs.
• Scalability & Resiliency – Designing systems that can handle sudden spikes in call volume without dropping requests or overloading AI APIs.
• Advanced concepts (less common) – Designing feedback loops for continuous model improvement and implementing A/B testing infrastructure for AI features.

Example questions or scenarios:

• "Design the backend architecture for a real-time sentiment analysis tool that monitors live support calls."
• "How would you structure a vector database and retrieval system to allow users to semantically search through years of call transcripts?"
• "Walk me through how you would scale an AI transcription service from 1,000 calls a day to 1,000,000 calls a day."

Key Responsibilities

As an AI Productivity Engineer, your day-to-day work revolves around identifying operational bottlenecks and solving them through intelligent automation. You will primarily focus on designing, building, and deploying AI-driven features that integrate directly into the Aircall platform. This includes developing robust RAG pipelines that allow users to query their historical call data and building automated workflows that extract key CRM data from voice conversations.

You will collaborate closely with product managers to define the scope and user experience of these AI features. Instead of just receiving technical specs, you will actively participate in product discovery, advising the team on what is technically feasible with current LLM capabilities. You will also work alongside core backend engineers to ensure your AI microservices communicate seamlessly with our main telephony infrastructure.

Beyond external product features, you will drive internal productivity initiatives. This involves building AI tooling for our own sales, support, and engineering teams—such as automated code review assistants, customer support ticket routers, or internal knowledge-base chatbots. You will be responsible for the entire lifecycle of these tools, from initial prototyping to monitoring their performance and cost in production.

Role Requirements & Qualifications

To thrive in this role at Aircall, you need a strong foundation in backend engineering coupled with hands-on experience in modern AI development. We are looking for builders who are passionate about productivity and user impact.

• Must-have skills – Deep proficiency in Python and modern backend frameworks (e.g., FastAPI, Django).
• Must-have skills – Proven experience building applications with LLMs and frameworks like LangChain, LlamaIndex, or raw API integrations.
• Must-have skills – Strong understanding of prompt engineering, context management, and vector databases (e.g., Pinecone, Weaviate, or pgvector).
• Must-have skills – Solid grasp of distributed system design, asynchronous processing, and API development.
• Nice-to-have skills – Experience working with voice data, speech-to-text (ASR) models, or telecommunications infrastructure.
• Nice-to-have skills – Familiarity with MLOps practices, model evaluation frameworks, and CI/CD for AI applications.
• Nice-to-have skills – Background in building internal developer tools or productivity software.

Frequently Asked Questions

Q: How much preparation time is typical for this role? Most successful candidates spend 2 to 3 weeks preparing. Focus your time heavily on practicing AI-specific system design and reviewing modern LLM integration patterns, rather than grinding obscure algorithmic puzzles.

Q: What differentiates a good candidate from a great candidate? Great candidates focus on the user and the business problem first, and the technology second. They do not just know how to build a complex RAG pipeline; they can clearly articulate why it is necessary and how it directly improves Aircall's productivity metrics.

Q: What is the working style like for the AI Productivity team? The team operates with a high degree of autonomy and a strong bias for action. You will experience a fast-paced, iterative environment where rapid prototyping is encouraged, and cross-functional collaboration with product and design is a daily occurrence.

Q: Is this role fully remote, or is there a hybrid expectation? This specific AI Productivity Engineer position is based in San Francisco, CA. Aircall generally supports a flexible hybrid model, but you should expect to be in the office a few days a week to collaborate closely with the local engineering and product hubs.

Q: What is the typical timeline from the initial screen to an offer? The entire process usually takes between 3 to 5 weeks. We strive to provide rapid feedback after each round and will work with your timeline if you are managing competing deadlines.

Other General Tips

• Focus on the "Why": Whenever you propose a technical solution, immediately follow up with the business or user justification. Aircall interviewers want to see that you understand the impact of your code.
• Master the Edge Cases: In AI engineering, the happy path is easy. Differentiate yourself by proactively discussing edge cases, such as handling garbled audio transcripts, managing LLM hallucinations, or dealing with API timeouts.

• Think in Systems, Not Just Scripts: While Jupyter notebooks are great for prototyping, we need engineers who build production software. Emphasize your experience with CI/CD, testing, monitoring, and robust API design during your technical rounds.
• Clarify Before Building: During system design and coding rounds, spend the first few minutes asking clarifying questions. Define the scale, the latency requirements, and the expected user behavior before you write a single line of code or draw a single box.

Summary & Next Steps

Joining Aircall as an AI Engineer is a unique opportunity to shape the future of business communication. You will be working at the intersection of high-volume voice data and cutting-edge generative AI, building tools that directly enhance the productivity of thousands of users and internal teams. The challenges you will face here—from optimizing low-latency inference to designing scalable RAG architectures—are complex, highly visible, and deeply rewarding.

The compensation data above reflects the base salary range of $160,000 -$220,000 USD for this position in San Francisco. When evaluating an offer, remember that your total compensation package will likely also include equity, comprehensive health benefits, and performance bonuses. Where you fall within this range will depend heavily on your performance in the system design and applied AI evaluation rounds.

As you finalize your preparation, focus on synthesizing your backend engineering skills with your applied AI knowledge. Review your past projects, practice articulating your design decisions out loud, and get comfortable discussing the trade-offs inherent in building production AI systems. You can explore additional interview insights, practice questions, and peer experiences on Dataford to further sharpen your edge. You have the technical foundation required to excel—now it is time to demonstrate your ability to execute and drive impact. Good luck!