Blog
Build real systems, understand production trade-offs, and prepare for AI engineering interviews.
Break into AI engineering and plan your transition from your current role.
Retrieval-augmented generation — chunking, vector search, reranking, grounding, and evals.
Tool-using agents: planning, memory, control flow, and safety.
Evals, observability, cost, latency, safety, and deployment.
AI engineer interviews: RAG, system design, and take-home projects.
A framework for GenAI and RAG system design interviews — the questions interviewers ask, a worked example, and what separates a senior answer from a junior one.
Build a tool-using AI agent from first principles — the agent loop, function calling, memory, and the guardrails that make it safe enough to ship.
A realistic look at AI engineer compensation in 2026 — the ranges, what actually moves them, and how to climb the band faster as a working developer.
The current, developer-first path into AI engineering: the skills that matter in 2026, what changed, and a realistic timeline you can follow around a full-time job.
You already build APIs, services, and databases. Here is the exact 90-day path a backend engineer can follow to become a production-ready AI engineer.
A practical, developer-first path into AI engineering: the skills that matter, the projects that get you hired, and a 90-day plan you can follow around a full-time job.
Data engineers already own ingestion, pipelines, and storage — the hardest parts of RAG. Here is how to turn those skills into an AI engineering role.
The AI job landscape is confusing. Here is a clear, developer-focused breakdown of AI Engineer, ML Engineer, GenAI Developer, Agentic AI Engineer, and Forward Deployed Engineer roles.
LLMOps is DevOps applied to AI systems. If you already run CI/CD, observability, and cost control, you are most of the way to an AI platform role.
A demo is easy. A GenAI system that survives real users is not. Here are the layers that separate a prototype from production: retrieval, evals, observability, cost, and safety.
Agentic AI, explained without the hype. What an agent actually is, how it differs from a workflow, and when you should (and shouldn't) build one.
A practical, developer-first walkthrough of building a retrieval-augmented generation app that is ready for real users — from ingestion to evals.
The questions AI engineering interviews actually ask — RAG, agents, evals, cost, and system design — with strong, developer-level sample answers.
Certificates are noise; projects are signal. Five portfolio projects that prove you can build, harden, and ship real AI systems — with what each one demonstrates.