After this lesson, you will be able to: Translate code-first agent skills into a resume, portfolio, and interview prep for AI engineer and agent-builder roles.
Code-first agent engineering is the most directly-compensated specialisation in this track. This lesson maps it to the actual hiring market.
AI Engineer / Agent Engineer, ships agents inside product teams. $140-$250k. Forward-Deployed Engineer (Anthropic, OpenAI, agent-startups), customer-facing agent builder. $200-$350k. Senior AI / ML Engineer, deeper systems + research. $200-$400k. Founding Engineer at agent startups, equity-heavy, ground-floor. $150-$220k base + 0.5-2% equity. Research Engineer (Anthropic, OpenAI, DeepMind), bridges research and production. $250-$500k. Search 'agent engineer', 'AI engineer', 'forward-deployed', 'applied AI' on LinkedIn.
Skills: Python, TypeScript, Anthropic API, OpenAI API, LangChain v0.2+ / LangGraph, LlamaIndex, CrewAI, MCP (server + client), RAG (chunking, embeddings, vector stores), evals (Promptfoo / LangSmith), Next.js for agent UIs, FastAPI for agent backends, Docker, observability (LangSmith / LangFuse), cost + latency engineering. Projects: 'Production-grade research agent (Anthropic + Next.js + Vercel). 30-case eval set, $0.04/run cost target, deployed at <url>.' 'MCP server exposing 4 tools to Claude Desktop; published on GitHub.' 'Multi-agent supervisor system using LangGraph; measured against single-agent baseline.' Certs: none canonical. Anthropic Academy completion is the cleanest current signal.
'Walk me through the agent loop you implemented from scratch.' (Tests aa-cf-06 raw-SDK depth.) 'How do you bound the cost of a runaway agent?' (Max turn cap, cost telemetry, kill switch.) 'Compare LangChain, LangGraph, CrewAI, and raw SDK, when do you pick each?' 'How would you defend an agent against indirect prompt injection?' (Tests aa-cf-07 + ai-10.) 'Tell me about a failure mode you found in production and how you fixed it.' 'How do you measure whether a multi-agent system is actually better than a single agent?'
Three of these in 60 days lands AI / agent engineer interviews.
Ship the passion project (aa-cf-passion-project) with deploy URL, eval set, cost story, case study.
Build and publish a public MCP server that wraps a real tool (Notion, Linear, Stripe sandbox).
Contribute to an open-source agent framework (LangGraph, CrewAI, Pydantic AI). Even a doc PR.
Write a deep-dive blog post: 'How I cut my agent's cost by 70%' or 'Five failure modes my agent hit in production'.
Record a 5-min Loom walking through the agent architecture for one of your projects.
Listing 'LangChain' without an opinion on when to use it vs raw SDK vs LangGraph. No public artefacts. GitHub + deployed URL + blog is the table-stakes set. Skipping the multi-agent honesty conversation. Acknowledging when single-agent wins shows seniority. Forgetting prompt-injection defences. AI security questions are a default in 2026 interviews. Underselling cost engineering. Cost is what makes AI features ship or get killed.
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