Learn By Doing In Real Production Environment

Get direct CTO mentorship and hands-on experience solving engineering problems that meet production level standards.

The Gap Between Learning and Production

Tutorials build familiarity. Production builds engineers.

No Real System Exposure

You’ve completed projects. But you’ve never operated inside a real, evolving production system.

No Senior-Level Feedback

Tutorials don’t evaluate how you think. Without architectural review and real critique, you don’t know what you’re doing wrong.

No Real Accountability

In production, decisions have consequences. Real responsibility builds judgment, confidence, and engineering maturity.

How Modern Engineers Build Systems

We treat AI as an engineering tool — not a substitute for system thinking. Participants learn how to combine modern AI-assisted development workflows with disciplined architecture, data modeling, and software engineering practices.

AI-Assisted Engineering Environment

Participants begin by configuring a modern development environment where AI tools are integrated directly into the engineering workflow. Instead of treating AI as a code generator, participants learn how to collaborate with LLMs while maintaining architectural thinking and engineering control.

The environment combines AI-native IDE workflows with structured prompting, repository-aware context, and disciplined verification practices inspired by modern AI engineering workflows.

  • AI-native IDE environments with integrated LLM assistants
  • Repository-aware context for architecture and code reasoning
  • Structured prompting workflows for iterative development
  • AI-supported debugging, code exploration, and documentation
  • Git-based development workflow with review and verification
  • Optional use of self-hosted models (e.g. Ollama) for private AI workflows

How Learn By Doing Transform You

A structured transition from foundational knowledge to production-level responsibility.

01

Production Exposure

You work on meaningful, real-world projects — not artificial exercises. Your deliverables reflect real constraints and real environments.

02

CTO-Level Technical Mentorship

Your work is evaluated across the full software development lifecycle — from requirements and architecture to implementation and deployment.

03

Beyond Code Capability

Communication, domain reasoning, and system-level awareness. Learn how technical decisions connect to business outcomes.

04

Strategic Positioning

Your domain expertise is converted into a strategic advantage. Your CV, online presence, and marketplace positioning reflect real production capability — not tutorial projects.

05

Structured Career Transition

After evaluation, you receive a structured direction for transitioning into paid work — aligned with your skills and domain expertise.

Case Study

From Internship to AI Engineer

Tala — Computer Engineering graduate from Birzeit University

Starting Point

Tala joined the program after completing her Computer Engineering degree at Birzeit University. She already had experience with Python, SQL, AI engineering concepts, and working with LLMs and data science tools.

Before joining, she had completed internships and freelance projects focused on backend development and automation. However, she wanted to move beyond smaller assignments and gain exposure to a larger commercial project where she could work with real business data and engineering teams.

Her motivation for joining the program is also reflected in her Trustpilot review .

Project During the Program

During the program, Tala worked on a real client project focused on building an autonomous AI-driven system designed to help a company orchestrate and manage its operations.

  • Proposed performance metrics based on business processes and operational data
  • Designed approaches for aggregating data from multiple internal tools and systems
  • Implemented data pipelines to analyze operational data
  • Communicated directly with business stakeholders to understand requirements
  • Collaborated with engineers working on the system architecture

Technologies Used

  • Python
  • Apache Airflow
  • SQL
  • Pandas
  • Docker
  • Airtable
  • External APIs and business system integrations

All contributions were reviewed through engineering feedback sessions and mentorship discussions focused on architecture decisions, data workflows, and production-quality code.

Result

Through the program, Tala gained hands-on experience building analytics systems and working with real operational data used by a client company.

The analytics developed during the project were used to support business decision-making and improve performance metrics across the client’s operations.

After completing the program, Tala received an offer from Hyperli where she now works as a Software Engineer / AI Engineer and Full-Stack Developer, continuing to build intelligent systems and production software.

TF
Tala
AI Engineer
★ ★ ★ ★ ★

"I joined Right Programmers through their internship program and got the chance to work on a real project with real outcomes, not just toy tasks. The learn-by-doing approach gave me hands-on experience in data analytics and working with real systems."

View full review on Trustpilot →

What We Build During the Program

Participants don't just study engineering concepts — they build real system components that combine data engineering, architecture design, and AI-assisted development workflows.

AI Context Systems with MCP

Participants build Model Context Protocol (MCP) integrations that expose structured system data to AI assistants and developer tools. Instead of relying on isolated prompts, AI models receive access to structured business context, data schemas, and operational APIs.

Through these integrations, engineers learn how modern AI-enabled systems provide context-aware interactions between software platforms and LLM-based tools.

  • MCP servers exposing system context and domain knowledge
  • Integration of internal APIs, services, and operational data
  • Structured context pipelines for AI assistants and developer tools
  • Secure access layers for exposing system capabilities to AI
  • Architecture patterns for integrating AI into existing systems

Data Engineering & Analytics Systems

Participants design and implement data pipelines that aggregate and transform information from multiple operational systems. These pipelines create structured data layers used for analytics, reporting, and operational decision-making.

The resulting architecture combines traditional data engineering practices with modern analytics and AI-assisted data enrichment techniques.

  • Data ingestion from multiple business systems and APIs
  • ETL / ELT pipelines orchestrated with Apache Airflow
  • Workflow automation for analytics and operational processes
  • Data warehouse schema design and transformation pipelines
  • Analytics datasets for BI and reporting systems
  • AI-assisted enrichment and analysis of structured datasets

Program Journey

Build a real system from business problem → architecture → implementation.

Week 1
AI-Assisted Engineering Environment
Environment & Workflow Setup
Set up a modern AI-assisted engineering environment where AI tools support development while maintaining architectural thinking and engineering discipline.
AI Native IDE • Context Engineering • MCP Integrations
Week 2
Career Strategy & Engineering Profile
Positioning & Skill Analysis
Participants evaluate their current capabilities, identify realistic entry roles, and define a professional positioning strategy aligned with real market demand.
Competency analysis • Career roadmap • Role positioning
Week 3
Business Requirements & System Understanding
The real project begins. Participants work with a business problem and learn how engineers translate unclear business needs into structured system requirements. This is the first step in building a real system.
BRD • Functional Requirements • User Stories
Week 4
Business Process & Information Flow Design
Participants analyze how the organization actually operates and visualize how information moves between people, systems and data.
AS-IS / TO-BE BPMN • Information Flow Diagrams • Gap Analysis
Week 5–6
Data Modeling & Data Engineering Foundations
The system's data layer is designed. Participants learn how business concepts translate into structured data models and engineering decisions.
Data Dictionary • ER Diagram • Data Flow Design • ETL concepts
Week 7–8
System Architecture & Domain-Driven Design
Participants transform business understanding and data models into a scalable system architecture using Domain-Driven Design and microservice thinking.
C4 Architecture • Domain Context Map • Microservice Design
Week 9–11
Architecture → Implementation Planning
The architecture is translated into real engineering tasks. Participants learn how professional teams convert system design into executable work.
Implementation task backlog • SDLC planning • Use-case mapping
Week 12
Career Profile & Job Preparation
Participants convert their project experience into a professional profile ready for the job market.
CV • LinkedIn • GitHub portfolio • Interview preparation
<strong data-start="542" data-end="570">Learn By Doing</strong>

Inside the Program

A real welcome message recorded for participants of Learn By Doing. No marketing — just how the program actually operates.

What This Means for You

  • You operate inside real production constraints — not controlled exercises.
  • Your technical decisions are reviewed at architectural level.
  • Your deliverables become real experience and testimonials.
  • Your domain expertise is strategically leveraged, not discarded.
  • Your readiness is evaluated against production standards — not assumptions.

Investment

An investment in real project experience and your first role in the IT industry.

Regular price: 1160 EUR
930 EUR
Discount available after passing the technical fit test
✔ ~12 week practical program ✔ Real system design project ✔ Mentoring with system architect / CTO ✔ Portfolio-ready project artifacts ✔ Career feedback and positioning ✔ LinkedIn & CV optimization ✔ Interview preparation
Flexible Payment Options
✔ One-time payment → full discount 20% (930 EUR)
✔ 2 installments → 15% discount (988 EUR)
✔ 3 installments → 10% discount (1046 EUR)
Job Guarantee. If you complete the program and follow the recommended career steps but still do not obtain your first role in IT, the full program fee will be refunded.

Application Process

A simple process designed to ensure the program is the right fit.

1
Submit the application form and share basic information about your background and experience.
2
Complete a short analytical / technical test used to evaluate your current level and potential.
3
Conversation with the mentor to discuss goals, expectations and program fit.
4
Agreement, payment setup and onboarding. After that you start the program.
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