AI Reliability Engineer (AIRE) Explained — Salaries, Skills, and How to Break In
What an AI Reliability Engineer actually does, why postings from Anthropic and OpenAI pay $250K-$485K, and the real skill gap between an AIRE and a classic SRE.
Showing 13 articles in AI Engineering
What an AI Reliability Engineer actually does, why postings from Anthropic and OpenAI pay $250K-$485K, and the real skill gap between an AIRE and a classic SRE.
Raw file dumps blow up token costs and wreck reasoning in agentic coding tools. Here's how AST signature stripping and dependency-aware context loading cut repo context by 90% or more.
A two-tier memory pattern for NestJS AI agents — Redis for the last few turns, pgvector for permanent facts — with the code, the tradeoffs, and where it breaks.
How to parse complex PDFs, nested tables, and multi-column layouts with Gemini's Files API, structured JSON schemas, and DPI tuning — without burning your token budget.
A single prompt can't safely refactor a multi-file codebase. Here's how repo-scoped slash commands and staged subagents in Claude Code fix that, with git safety gates included.
A cost and latency comparison of self-hosted headless ComfyUI against managed APIs like Replicate for Flux and SDXL image generation in web apps.
Two-tier progressive discovery keeps coding agents like Claude Code fast by loading skill instructions only when a task actually needs them.
A production playbook for eliminating JSON truncation, schema drift, and markdown pollution in LLM structured outputs — with real code for constrained decoding, chunking, and repair layers.
A practical breakdown of hash fingerprinting, budget caps, and temperature perturbation for stopping autonomous LLM agents from getting stuck in tool-call loops.
How a single locked master prompt with four variable fields keeps an entire brand's AI-generated carousel visuals consistent — and why the AI should never touch your text.
Why long AI coding sessions quietly degrade, and four concrete practices — repo rule files, short sessions, symbol ingestion, ADRs — that fix it.
A practical three-tier workflow for catching the subtle bugs, boundary omissions, and security gaps AI coding assistants keep shipping.
Local MCP servers usually fail in production for one of four reasons: transport, payload size, tool naming, or auth handshakes. Here's how to fix each.