Stage 1 · The six-layer architecture
1 week
Model layer, orchestration, retrieval, evaluation, observability, gateway + policy. The mental map every production AI system maps onto.
View AI Engineering Mastery →Cybersecurity and Applied AI career intelligence
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Founded by Julian Calvo, Ed.D., M.S.
Applied AI · Engineer → AI engineer
DecipherU's AI engineering track takes working software engineers to production-AI competence in 12 to 18 weeks. You will ship a working RAG application, an agent with tool-call discipline, an evaluation harness with ground truth, and a cost model with prompt-caching applied.
The AI engineering track for working software engineers. RAG, agents, evaluation, observability, cost discipline.
What this path pays
$130K → $215K-$330K
BLS median for Software Developer (15-1252) is $130,160. AI engineer roles in 2026 cluster at $215-330K total comp at top-quartile employers (Lightcast 2024 AI premium overlay).
Source: BLS OES May 2024 + Lightcast AI premium series 2024
Why this path
AI engineering pay tracks 1.4-2.2× the median SWE band in 2024-2026 Lightcast data. The differentiator at hiring is not 'know what an LLM is' — every engineer knows that now. It is the operational discipline: eval harnesses with ground truth, gateway policy, observability with cost + quality dimensions, threat modeling against OWASP LLM Top 10. This track teaches all of it.
Stage 1 · The six-layer architecture
1 week
Model layer, orchestration, retrieval, evaluation, observability, gateway + policy. The mental map every production AI system maps onto.
View AI Engineering Mastery →Stage 2 · RAG that actually works
3-4 weeks
Beyond toy examples. Hybrid search, reranking, query rewriting, HyDE, evaluation against ground truth, document-level citation tracing.
View AI Engineering Mastery →Stage 3 · Agents + tool calls
3-4 weeks
Tool-call discipline, error recovery, excessive-agency defenses, cost of agentic loops. The patterns that survive in production.
View AI Engineering Mastery →Stage 4 · Eval-first development
2-3 weeks
Capability + stability + behavioral layers, ground truth construction, LLM-as-judge, regression evals, online evals. Eval is the gate that lets you ship.
View AI Engineering Mastery →Stage 5 · Production capstone
2-3 weeks
Ship a production AI system against a documented scope, with eval harness, cost model, threat model, observability, and rollout plan.
View AI Engineering Mastery →Real at top-quartile AI employers (foundation labs, AI-first startups with funding, hyperscalers' AI organizations). Median across the broader market is closer to $185-240K. Public-disclosure data from levels.fyi and Lightcast 2024 show the premium is consistent across major metros, with 80-95% concentrated in CA, WA, NY, MA.
No. AI engineering is the practitioner discipline of building reliable systems on top of pretrained models. ML research is a separate (and much smaller) market. The differentiator at hiring is operational discipline (eval harnesses, gateway policy, threat modeling) — not ML theory.
Andrew Ng's courses are excellent for ML foundations but stop at the practitioner-engineering boundary. This track picks up where they end: production observability, agent reliability, gateway design, OWASP LLM Top 10 controls, cost discipline, vendor selection. The two are complementary; this one is closer to what hiring teams actually test for AI engineer roles.
The AI Product Management persona may fit you better — it teaches the same AI-system mental model without requiring you to write production code. If you do write code professionally and can ship features, this engineering track is the right one.
Where this path meets the other vertical
AI engineers who can defend their own systems against prompt injection, agent abuse, and supply-chain attacks command a measurable premium and tighter hiring pipelines.
See the convergence persona →