AI Engineer: the complete path
Everything an AI engineer goes through, from first day to production: the role, Python and software foundations, ML basics, LLM engineering, RAG, agents and MCP, fine-tuning, evals, LLMOps and deployment, data pipelines, security and governance, system design, collaboration, and a 12-month roadmap with projects and interview prep.
AI Engineer: The Complete Path
Everything an AI engineer goes through, from first day to production: the role, Python and software foundations, ML basics, LLM engineering, RAG, agents and MCP, fine-tuning, evals, LLMOps and deployment, data pipelines, security and governance, system design, collaboration, and a 12-month roadmap with projects and interview prep.
In one line
An AI engineer builds, ships and runs AI-powered software: calling and customising models, connecting them to data and tools, testing quality, deploying reliably, keeping cost, safety and security under control, and improving the system with real usage.
The 12 classes
The AI engineer role and the lifecycle
Know what the job is, how it differs from neighbouring roles, and the lifecycle you'll work in.
- AI engineering = software engineering + models + evals + operations.
- Work in a loop: define, build, evaluate, deploy, improve.
- Neighbouring roles overlap; know the boundaries.
Foundations: Python, software engineering, Git, APIs, Linux
Build the engineering base that every AI system sits on.
- Python + testing + Git + HTTP are non-negotiable.
- Handle rate limits and retries from day one.
- Never put secrets in code.
Math, statistics and machine-learning essentials
Know enough ML to choose methods, read metrics and debug models.
- You don't need a PhD: need metrics, validation and intuition.
- Embeddings and similarity are linear algebra in use.
- scikit-learn first, PyTorch when needed.
LLM engineering: model APIs, prompts, structured output, cost and latency
Build reliable features on top of model APIs.
- Treat the model as an unreliable remote service: validate, retry, fall back.
- Track tokens, cost and latency on every call.
- Structured outputs make LLMs usable in code.
RAG, embeddings and vector databases
Build search and question-answering over private data, and measure it.
- Retrieval quality limits answer quality.
- Hybrid search + reranking beat vectors alone.
- Measure retrieval and generation separately.
Agents, tool use and MCP
Build agents that call tools, with control and observability.
- Agents = LLM + tools + loop + guardrails.
- Start with simple workflows; add autonomy when evals justify it.
- MCP standardises tool access.
Customising models: fine-tuning, distillation and open-source models
Decide when to fine-tune, and how, and when not to.
- Prompting → RAG → fine-tuning, in that order.
- Data quality beats data quantity.
- Always compare with a prompting baseline.
Evals, observability, guardrails and red-teaming
Make quality measurable and keep it that way.
- No eval, no launch.
- Trace everything to debug fast.
- Guardrails are layers, not a single filter.
Deployment and LLMOps: APIs, containers, cloud, CI/CD, scaling and cost
Ship AI services that are reliable, fast and affordable.
- Containerise, test, evaluate, deploy, monitor.
- Use a gateway for routing, caching and budgets.
- Version prompts and models like code.
Data engineering for AI, security and governance
Feed AI systems good data and keep them safe and compliant.
- Good data pipelines keep RAG fresh and safe.
- Know the OWASP LLM risks and their controls.
- Least privilege for every agent tool.
System design for AI products and working with the team
Design end-to-end AI systems and collaborate across roles.
- Always state trade-offs and failure handling.
- Evals, safety and cost belong in every design.
- Great AI engineers translate between product and models.
Career: roadmap, portfolio projects, interviews and job descriptions
Plan 12 months, build proof, and prepare for hiring loops.
- Build proof, not just courses: 10 projects.
- Prepare coding, ML, LLM, system design and behavioural rounds.
- Match your portfolio to real job descriptions.
Interview questions
What does an AI engineer do?
Builds, evaluates, deploys and operates AI-powered software: model APIs, RAG, agents and tools, evals, guardrails, cloud deployment and monitoring, with attention to cost, latency and safety.
AI engineer vs ML engineer?
AI engineers mostly build products on existing models (prompts, RAG, agents, evals); ML engineers train and serve custom models and run ML pipelines. The roles overlap.
How would you design a RAG system?
Ingest and chunk documents with metadata, embed, store in a vector DB, retrieve with hybrid search and reranking, generate with citations, enforce access control, evaluate retrieval and faithfulness, monitor and refresh.
When do you fine-tune?
After prompting and RAG fall short: for consistent style or format, narrow tasks, or to lower cost with a smaller model; compare to a prompting baseline on held-out data.
How do you evaluate an LLM application?
Golden dataset with slices, code and LLM-judge graders calibrated to humans, regression tests in CI, online metrics and sampled judging.
How do you reduce hallucinations?
Ground answers in retrieved sources, require citations and 'not found', use structured outputs, add verification steps and evals, and escalate low-confidence cases.
How do you control cost and latency?
Route to smaller models, cache, trim context, stream, batch, set budgets, and track cost per successful task.
What are the main security risks of LLM apps?
Prompt injection, data leakage, insecure output handling, excessive agency, supply chain and denial of wallet; mitigate with least privilege, validation, sandboxing, approvals and rate limits.
