Machine Learning/ AI Engineer

Job Description

Joining Busuu means being part of one of the top EdTech companies in the world, a multiple award-winner recognised for its innovation and impact in language learning.

Busuu's vision is to empower people through languages. We are the world's largest online community for language learning, with 120+ million registered users. We make learning a language easy by combining AI-powered courses with feedback from our global community of native speakers and lesson content designed for real life.

Busuu is part of the global Chegg family. Chegg is the leading student-first connected learning platform and a NYSE listed company.

• Build agentic AI systems: Design, develop and deploy production-grade agentic systems that power adaptive learning experiences — from multi-step reasoning pipelines to autonomous feedback loops that respond to learner behaviour in real time.

• LLMs & RAG architectures: Architect and integrate LLM-powered features using retrieval-augmented generation (RAG), prompt engineering strategies, and evaluation pipelines. Apply these to use cases such as mistake analysis, content generation, and personalised learning paths.

• Agentic frameworks: Use frameworks such as LangChain and LangGraph to build reliable, observable multi-agent workflows. Design agent orchestration patterns, tool use, memory, and state management for production environments.

• ML system development: Collaborate with Senior ML Engineers and Data Scientists to move models from experimentation to production, including feature engineering, training pipelines, online inference, and monitoring.

• Platform & tooling: Contribute to our ML infrastructure and experiment orchestration tools (e.g. MLFlow, Airflow, SageMaker), and help make AI development faster and more reliable across the team.

• Cross-functional collaboration: Work closely with Data Engineers, Product Managers, and other engineers to embed intelligence into our products, improve experimentation velocity, and drive measurable learning outcomes.

• Research & innovation: Explore emerging AI/ML technologies — from graph-based knowledge representations to advanced RAG patterns and recommender systems. Help shape our evolving AI strategy.

• Solid foundations in machine learning and software engineering. You write clean, maintainable Python code and are comfortable designing ML pipelines and working on APIs and microservices.

• Hands-on experience with LLMs (e.g. OpenAI, Anthropic, HuggingFace) and practical knowledge of prompt engineering, vector stores, and RAG architectures.

• Experience building agentic systems using LangChain and/or LangGraph — including agent orchestration, tool use, and multi-step reasoning pipelines in production or near-production settings.

• Familiarity with building data and training pipelines using tools like SQL, Airflow, or AWS services (S3, SageMaker, Lambda).

• Some exposure to deploying ML systems to production, ideally around NLP, personalisation, or recommendation. A/B testing experience is a plus.

• Graph-based data structures or graph databases (e.g. Neo4j, NetworkX) is a strong plus.

• Strong analytical thinking and genuine curiosity about the user experience and pedagogical impact of AI solutions.

• Good communication skills and a collaborative mindset. You take ownership, iterate quickly, and ask for help when you need it.

• Experience in EdTech, adaptive learning, or consumer personalization is a nice-to-have, not a requirement.

• Centrally located offices with free breakfast, snacks, and fresh fruit

• 2 free lunches per week from a wide selection of restaurants

• Great Private Health Insurance scheme

• Personal training budget to keep growing

• Flexible working hours and a hybrid model of working

• Enhanced maternity and paternity leave

• Frequent social activities: team lunches, Thursday socials, quarterly events

CV review – We'll review your application as quickly as possible.

Let's chat – A quick call with our team about your experience and the role.

First Interview – With the Hiring Manager.

Cultural Interview – Interview about soft skills and value alignment with the team.

Technical Interview – Structured questions covering core AI/ML engineering knowledge with team members.

Our platform is for everyone, and so is our workplace. We embrace our differences — cultural, racial, religious, or otherwise — and believe every voice matters.

If this sounds like the kind of place where you could thrive, we'd love to hear from you.