AI / ML Engineer
AI/ML engineers build systems that learn from data. This path covers the vocabulary for discussing model architectures, training pipelines, evaluation metrics, and the rapidly evolving language of LLMs, embeddings, and RAG — used daily in design reviews, paper discussions, and stakeholder updates.
Topics covered
- ML fundamentals
- LLMs & foundation models
- RAG & embeddings
- MLOps & deployment
- Model evaluation
- Responsible AI
Vocabulary spotlight
4 terms every AI / ML Engineer should know in English:
When an LLM generates plausible-sounding but factually incorrect information
"The model hallucinated a citation — always verify AI-generated references."
Adapting a pre-trained model to a specific task by training it on domain-specific data
"We fine-tuned GPT on our support tickets to improve its product knowledge."
Vector representations of text that capture semantic meaning for similarity search
"We store document embeddings in a vector database for semantic retrieval."
The maximum amount of text an LLM can process in a single inference call
"The 200k-token context window lets us pass the entire codebase to the model."
📚 Vocabulary Reference
Key terms organised by category for AI / ML Engineers:
ML Fundamentals
LLMs & Transformers
RAG & Retrieval
MLOps
Recommended exercises
Real-world scenarios you'll practise
- Explaining model evaluation metrics to a non-technical product manager
- Presenting RAG architecture trade-offs in a system design review
- Writing an ML model card for internal governance
- Discussing responsible AI concerns with a compliance officer
🎯 Interview questions specific to this role
Practise answering these questions out loud — or in writing. Each question targets a real interviewer concern for AI / ML Engineers.
- What is the difference between a transformer and an RNN?
- How do you evaluate the quality of an LLM-based application?
- What is retrieval-augmented generation and when would you use it?
- How do you detect and mitigate bias in a machine learning model?
- Walk me through your MLOps process from model training to production deployment.
Recommended reading
Frequently Asked Questions
What English skills do AI / ML Engineers most need to improve?+
AI / ML Engineers most commonly need to improve: technical vocabulary (the correct English terms for domain concepts), collocation accuracy (using the right verb for each action), written communication (bug reports, PR descriptions, technical docs), and spoken communication for standups, code reviews, and stakeholder meetings.
How long does the AI / ML Engineer learning path take?+
The AI / ML Engineer learning path contains 20–40 hours of material studied comprehensively. Most learners focus on the highest-priority modules first and return to the rest over time. Spending 30 minutes per day for 4–6 weeks produces noticeable improvement in workplace English.
What vocabulary should a AI / ML Engineer prioritise first?+
Start with the vocabulary that appears most in your daily work — terms you read in documentation, use in commit messages, and hear in meetings. The AI / ML Engineer path begins with the most frequent vocabulary clusters before moving to advanced communication patterns.
Are there interview exercises for AI / ML Engineer roles?+
Yes. The AI / ML Engineer path includes role-specific interview question modules with model answers and key phrases — the actual questions interviewers ask and the vocabulary needed to answer them fluently. There is also a dedicated Interview Practice hub for general interview skills.
Does this path include pronunciation help?+
Yes. The path links to pronunciation exercises for the technical terms most commonly mispronounced in this domain. The Pronunciation hub includes drills for acronyms, silent letters, word stress, and minimal pairs — all in IT context.
What are the most common English mistakes AI / ML Engineers make?+
The most common mistakes: incorrect collocations (using the wrong verb with a technical noun), false friends from L1, tense errors when narrating past incidents or walkthroughs, and using overly formal or overly casual register in written communication.
How do I improve my English for code reviews?+
Learn the standard code review collocations: approve a PR, request changes, leave a nit, address feedback, block a merge, resolve a conversation. Use hedging language for suggestions: "This might be cleaner as…", "Have you considered…?". The Collocations section includes a dedicated Code Review set.
Can I use this path alongside my daily work?+
Yes — the path is designed for working professionals. Each exercise set takes 10–15 minutes. The most effective approach is to study a vocabulary module before a meeting or task where you'll use that vocabulary, then practise immediately after. Context-linked practice produces much faster retention.
Is the content free?+
Yes, completely free. No registration required, no payment, no time limit. All vocabulary modules, exercises, glossary entries, and learning path guides are open access.
How do I track my progress through this path?+
Progress is tracked in your browser's local storage — completed exercise sets are marked with a checkmark when you return. No account is needed. You can bookmark specific modules and use the exercises overview to see which sets you've completed.