Blog
Practical guides on AI engineering, job search strategy, and getting hired.
The AI/PM Collision: Understanding Each Other
How to work effectively with product managers on AI features.
Interviewing for AI Roles: What They Actually Test
Beyond leetcode: what top companies are really looking for.
The Startup vs Big Tech Tradeoff for AI Engineers
Where you'll learn more, earn more, and have more impact.
Observability for AI Systems
Monitoring, debugging, and understanding AI applications in production.
Moving From ML Engineer to AI Engineer
What's different and what you already know.
Negotiating Your AI Engineer Salary in 2026
Market rates, negotiation tactics, and what to expect.
The Data Pipeline Problem in AI
Why 80% of AI project time goes to data engineering.
Open Source AI Models Worth Your Time in 2026
Which open models are production-ready and worth deploying.
The Interview Loop: System Design Round
What to expect and how to prepare for system design interviews at top tech companies.
Evaluating LLM Outputs: Beyond Vibes
Metrics and methods for objectively assessing language model quality.
Cost Optimization for LLM Applications
How to reduce API spending without sacrificing quality.
System Design for AI Systems
Architecting scalable, reliable AI applications in production.
The Most Overrated AI Skills Right Now
Skills everyone thinks are important but companies rarely hire for.
Building a Portfolio That Gets You Hired
Projects and demonstrations that actually impress hiring managers.
AI Safety for Product Engineers
Practical guardrails and safety measures for AI applications in production.
Fine-Tuning vs RAG: Which Should You Use?
When to customize models through fine-tuning and when retrieval is enough.
Why Your AI Project Is Slow (And How to Fix It)
Common latency bottlenecks in LLM applications and optimization strategies.
Vector Databases: Choosing the Right One
Comparing Pinecone, Weaviate, Qdrant, pgvector, and Milvus for your AI application.
Learning Prompt Engineering (Beyond the Memes)
A structured approach to becoming genuinely good at working with language models.
RAG Systems in Production: What Went Wrong
The most common failures in RAG implementations and how to avoid them.
The AI Engineer Skills Gap in 2026
Why companies can't find qualified AI engineers and what skills are actually worth learning.
Why Your Resume Gets Rejected Before a Human Reads It
Around 75% of resumes never reach a hiring manager. The filter isn't subjective — it's automated, and it follows rules you can understand and optimize for.
The Technical Interview Playbook for AI Engineers
AI engineering interviews are different from standard software engineering interviews — but most candidates prepare for the wrong thing. Here's what to actually study.
5 Portfolio Projects That Get AI Engineering Jobs
Most AI engineering portfolios have the same three tutorial projects. Here's how to stand out — with specific project ideas, what to build, and how to present them.
How to Get Hired as an AI Engineer in 2025
The job market for AI engineers has shifted fast. Here's what the data says about what companies are actually hiring for — and how to position yourself to land one of these roles.