After this lesson, you will be able to: Translate AI-track skills into a resume, portfolio, and interview prep that lands AI engineer or AI product roles.
AI engineering is one of the highest-paying entry points into tech in 2026. This lesson maps the sub-track skills onto the actual hiring landscape.
AI Engineer / Applied AI Engineer, ships AI features inside a product team. $130-$230k. Forward-Deployed Engineer (Anthropic / OpenAI / customer-facing), embeds with customers building on the API. $200-$350k. AI Product Manager, owns AI feature roadmaps. $150-$250k. Prompt Engineer (declining as a standalone title, still hired at scale shops), focuses on prompt + eval design. $130-$220k. ML Engineer, classical role training and deploying models. $150-$300k. Search 'AI engineer', 'applied AI', 'forward-deployed engineer' on LinkedIn.
Skills: Python, TypeScript, Anthropic API (Claude), OpenAI API, prompt engineering, eval design (Promptfoo / LangSmith), Next.js for AI feature delivery, Hugging Face for open-source baselines, RAG pipelines, prompt injection defences, cost + latency engineering. Projects: 'AI-powered document summariser built with Anthropic + Next.js + Vercel. 22 eval cases, $0.012/request cost target, deployed at <url>.' 'Custom Promptfoo eval suite for hallucination detection (GitHub).' 'Three blog posts on prompt-engineering failures I shipped and fixed.' Certs: AI security has no canonical cert yet. DEFCON AI Village participation, Anthropic Academy certificates, Hugging Face course completions all carry weight.
'Walk me through how you'd build an AI feature that summarises customer support emails.' (Tests product thinking + the four-questions-before-prompting framework.) 'How do you know when your AI feature regresses?' (Evals; if you can't answer this in 60 seconds you're not ready.) 'Describe a prompt injection you've defended against.' (Bring your own example.) 'What's the cost of your AI feature at 10K requests/day? Walk me through the math.' 'Why Anthropic over OpenAI for this use case?' (Test that you can argue model choice, not just pick a favourite.) 'Tell me about a time the model was wrong in production. What did you do?'
Two or three of these inside 60 days and you have a defensible AI-engineer portfolio.
Ship the passion project from ai-passion-project. Deployed URL + GitHub + case study.
Build a Promptfoo eval suite for a non-trivial task and publish it on GitHub.
Write 3 blog posts: 'A prompt-injection I shipped and fixed', 'How I cut my AI feature's cost by 60%', 'Three evals every AI feature needs'.
Contribute to an open-source AI tool (Anthropic SDK, Promptfoo, or a Hugging Face Space).
Run a small fine-tune via Hugging Face PEFT on a public dataset; publish to the Hub.
Listing 'used ChatGPT' as a skill. Everyone has. Show structured engineering instead. Building 10 demos instead of shipping 2 polished ones with case studies. Skipping the cost/latency story. Hiring managers know the model; they want to know if you can ship it economically. Pretending the model is more reliable than it is. Honesty about failure modes is a strong positive signal in AI interviews.
Sign in and purchase access to unlock this lesson.