AI / Agents Engineer
Go beyond prompting: build retrieval, tools, evals, and agentic loops that are reliable enough to ship.
Roadmap progress
0% 0 of 8 steps done
How to read the signals
- 1
LLM fundamentals
Tokens, context windows, temperature, embeddings. Know the primitives before the frameworks.
Importance High Market demand High Automation risk Low - 2
Prompt & context engineering
System design for words: structure, few-shot, output formats. Increasingly assisted by models themselves.
Importance High Market demand High Automation risk Medium - 3
Retrieval (RAG)
Chunking, embeddings, vector stores, hybrid + re-ranking. Where most real value lives today.
Importance High Market demand High Automation risk Low - 4
Tool / function calling
Let the model act: schemas, validation, safe execution. The bridge from chat to software.
Importance High Market demand High Automation risk Low - 5
Agentic loops & orchestration
Plan–act–observe, memory, multi-step tasks, MCP. Hard to make reliable — and that's the moat.
Importance High Market demand High Automation risk Low - 6
Evals & guardrails
Datasets, LLM-as-judge, regression tests, safety filters. You can't improve what you don't measure.
Importance High Market demand High Automation risk Low - 7
Serving & cost control
Streaming, caching, fallbacks across providers, token budgets. Make it cheap and fast in prod.
Importance Medium Market demand High Automation risk Medium - 8
Safety & responsible AI
Prompt-injection, PII handling, anti-overclaiming. Ship things you can stand behind.
Importance High Market demand Medium Automation risk Low