Vector Database Engineer Jobs
The AI Infrastructure Role

Every AI application needs vector search. Pinecone, Weaviate, Chroma experts
are earning $130K-$180K+ building AI's data layer.

Vector DBs Are the New Essential.
Expertise Is Scarce.

You see vector databases in every AI architecture but haven't worked with them at scale.

The ecosystem is fragmented: Pinecone, Weaviate, Chroma, Qdrant, Milvus.

Production vector infrastructure requires skills beyond basic insertion and search.

Master Vector Infrastructure.

The World-Class AI Engineer Cohort

Vector databases are becoming as fundamental to AI applications as relational databases are to web applications. Learn to build, scale, and operate vector infrastructure that powers production AI systems.

1

Vector Fundamentals

Embeddings, similarity search, indexing

2

Platform Deep Dive

Master 1-2 vector databases thoroughly

3

Production Operations

Scaling, optimization, monitoring

Meet Your Mentor

Zen van Riel

My aim has been the same for years: become a world-class AI engineer. Every career move I've made has been measured against that.

I started as a software tester on a $500/month internship in the Netherlands. Taught myself to code, learned to ship real systems, and worked my way to Senior Engineer at GitHub.

Then I left GitHub. I joined an AI research lab as Member of Technical Staff, where I currently build products for secure AI monitoring.

The cohort draws directly from my real experience so you can make progress fast.

I run this special cohort with only a few people because hands-on work with me is what it takes to bring you to become a world-class AI engineer.

Career progression from Intern to Senior Engineer

Real Results

Vittor

Vittor

AI Engineer

Built and deployed his portfolio piece, then landed the AI role

"The coaching played a huge part in my success. I focused on AI fundamentals, the certification path, and soft skills like professional writing. Having access to expert guidance gave me confidence during interviews and helped me feel I was on the right path.

I built my own platform (simple but functional) and deployed it on AWS. I used it in my portfolio and showcased it during interviews. The way complex topics were explained, especially the restaurant analogy for AI systems, really stuck with me. Focusing on doing the basics well was absolutely essential."

What You Will Get

8 Weekly Tuesday Sessions

3 hours each for 24 live hours total.

Project Scoping at Kickoff

We set the scope of what you'll ship and the milestones to get there before the live sessions start.

Code Reviews

Reviews of your code from Zen during the cohort.

Lifetime Demo Access

Every architecture demo is recorded and yours to keep.

Demo Day

You present what you built and get feedback from Zen, with a recording you can use in your portfolio.

12 Months Community Access

Included with the cohort.

Vector Infrastructure Expertise Is In Demand

8
Weeks
6
Seats per Cohort
24
Live Hours with Zen

Frequently Asked Questions

Why are vector database engineers in demand?

Every LLM application, recommendation system, and semantic search feature needs vector databases. As companies adopt AI, they need engineers who understand vector infrastructure: how to structure data for efficient retrieval, scale to millions of vectors, optimize query latency, and integrate with ML pipelines. This is infrastructure work that traditional DBAs don't know and ML engineers often aren't interested in.

Which vector database should I learn?

Start with one and learn it deeply. Pinecone is most common in production and managed (easier to start). Weaviate offers hybrid search and is popular in enterprise. Chroma is open-source and common in prototyping. Qdrant is gaining traction for its performance. The concepts transfer across platforms. Pick based on what companies in your target market use most.

How much time will this take?

You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.

I've signed up for cohorts before and dropped out. How is this different?

It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.

I'm not pivoting careers. I want to build a product. Does this still work?

Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.

Do I need prior AI experience?

You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.

What does it cost?

It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.

Can I do this while working full-time?

Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.

I accept those who have the highest chance of success.

In the 30-minute call we discuss your goals and whether you are ready for the program.