Live AI career upgrade for data engineers
Data Engineer→AI Engineer
Turn data infrastructure skills into production AI application skills. From pipelines and storage to semantic retrieval, RAG and AI data platforms — live, project-based, and built for experienced data engineers.
Live · Project-based · Experience required · Limited cohort
This is for you if…
This track is built for you
- ✓You currently work as a data engineer
- ✓You already understand pipelines, data modeling and storage
- ✓You have experimented with AI but haven't built retrieval or AI applications
- ✓You want practical skills rather than another theoretical course
- ✓You want work you can demonstrate in interviews or at your current company
You are not starting from zero. We build on the experience you already have.
The transformation
Data Engineer → AI Engineer
Where you are today
Data Engineer
You can:
- ✓ Build and run data pipelines
- ✓ Model data and manage warehouses
- ✓ Design storage, indexing and ETL systems
But you may not yet know how to:
- ✕ Build retrieval and RAG systems
- ✕ Ship LLM applications on your data
- ✕ Operate AI systems with evaluation and observability
Where we're taking you
AI Engineer
You will learn to:
- ✓ Build semantic retrieval and RAG systems
- ✓ Build context pipelines for AI agents
- ✓ Build AI evaluation data infrastructure
- ✓ Serve and scale AI applications
You won't finish with another certificate.
You'll finish having built something real.
By the end of the program, you will have built:
Learn by building
What You Will Build
Scan this. This is what you leave with — every project is a working artifact, not a lesson.
Project 01
AI-Ready Ingestion Pipeline
Turn raw data into clean, LLM-ready context.
Project 02
Semantic Search System
Build embeddings, vector search and metadata filtering.
Project 03
Enterprise RAG Application
Build retrieval, reranking, citations and context assembly.
Project 04
Agent-Ready Context Pipeline
Prepare structured and unstructured data for agent workflows.
Project 05
Evaluation Data Infrastructure
Build golden datasets, offline evaluation and regression testing.
Project 06
Production AI Data Architecture
Design for caching, throughput, observability and cost.
Capstone
Design and build a production AI data platform or application — from ingestion to a deployed, evaluated system.
Demo it · discuss it · prove it
Curriculum
Every module ends in an output
Each module is built around a capability you can use — not a topic list. Expand a module to see the key concepts behind what you'll build.
Your flagship project
Design and build a production AI data platform or application — from ingestion to a deployed, evaluated system.
Demo it to the cohort. Discuss it in interviews. Show it internally. Use it as proof of capability.
Your unfair advantage
You're closer to AI Engineer than you think.
You already understand pipelines, data modeling, storage, indexing and orchestration. AI engineering adds retrieval, embeddings and LLM applications on top of infrastructure you already run.
You already know
We add
Data pipelines
Data-to-LLM pipelines
Data modeling
Embeddings + chunking
SQL / warehouses
Vector retrieval
ETL
Ingestion for RAG
Orchestration
Agentic workflows
Data quality
AI evaluation
Why UPGRADE
Not another AI course
Built for professionals
No “what is Python?” or unnecessary from-zero content.
Learn by building
Real systems and outputs, not passive lectures.
Production thinking
Architecture, reliability, evaluation, security and deployment.
Proof of capability
Finish with work you can demo and discuss.
The format
How the live program works
01
Before each live session
Short preparation material so you arrive ready to build.
02
Live session
Instructor builds and explains concepts in real time.
03
Build assignment
You implement the system yourself.
04
Review
Feedback, debugging and architecture discussion.
05
Capstone
Combine everything into one meaningful project.
No endless recorded-video library. You learn by showing up and building.
Your instructors
Learn from practitioners who have built and led at top technology, finance and consulting companies.
UPGRADE is taught by experienced engineers, architects, product leaders and operators with professional backgrounds across organizations such as Google, Microsoft, Amazon, Goldman Sachs, BCG and Paytm.
Not career trainers teaching from slides. Practitioners teaching systems, decisions and lessons from real-world work.
- ✓ Experienced engineers, architects, product leaders and operators
- ✓ Built and shipped production systems at scale
- ✓ Enterprise AI architecture and implementation
- ✓ Decisions and lessons from real-world work
- ✓ Practitioners — not career trainers
Practitioner backgrounds include
What participants build
Real systems, not certificates
Every cohort ends with participants demoing what they built — real systems, not slide decks. As cohorts run, we'll replace this space with actual participant work and before/after stories.
- ✓ Production AI applications
- ✓ RAG systems with evaluation
- ✓ AI agents with tools and guardrails
- ✓ Working product prototypes
- ✓ Enterprise AI reference architectures
This program is not for everyone
Don't join if:
- ✕ You are looking for a completely passive course
- ✕ You cannot commit time to actually building
- ✕ You want only a certificate
- ✕ You have no relevant professional foundation for this track
Join if
- ✓ You already have experience in data engineer
- ✓ You want practical AI capability
- ✓ You're willing to build every week
- ✓ You want to become a AI Engineer
What this unlocks
Possible outcomes
Upgrade your current role
Use AI and retrieval systems to become substantially more capable in your data organization.
Move into an AI-native role
Build the skills required to credibly pursue AI Engineer roles.
Build proof
Have RAG systems and pipelines you can discuss in interviews.
Build independently
Stop being limited to dashboards and start shipping AI systems on your data.
Founding cohort
One program. One price.
Founding Cohort
Limited time- ✓Live sessions
- ✓Project builds
- ✓Recordings
- ✓Templates / source code
- ✓Cohort community
- ✓Capstone
- ✓Feedback & review
- ✓Certificate (if you want one)
No-risk first session
Our promise to you
Attend the first live session. If you believe the program isn't at the level promised, request a full refund within 7 days of the first session.
Questions, answered
No ML background required. Your data engineering foundation — pipelines, storage and indexing — is a direct on-ramp to RAG and retrieval systems.
Your existing experience got you here.
Now upgrade it for AI.
Data Engineer → AI Engineer
Build the capability. Build the proof. Make the transition.
Live · Project-based · Limited cohort