AI Engineer
Certification Program

ORCHESTRATE YOUR CAREER TO THE TOP

Become a Leading Engineer at the Forefront of AI

Every tech company is integrating AI into its products, infrastructure, and core workflows. The demand for engineers who can build production-grade AI systems, not just consume APIs, has outpaced the talent pool by a significant margin.

Most developers use LLMs daily. Very few know how to architect an Agentic AI pipeline, orchestrate a multi-agent system, or take an AI application from prototype to production with proper monitoring, failover, and compliance built in. That is why we built this training program.

The GDE AI Engineer program is built for senior developers who recognize that AI is reshaping what companies build, how they build it, and who they hire to do it. This program takes you from writing code that calls AI, to building AI systems that other engineers rely on.

The Skills Companies Are Looking For

From Agentic AI pipelines and multi-agent systems to MCP architecture, vLLM serving, and production observability, every module is built around skills that are in active demand at the companies hiring right now.

Build a Real AI Application

Over the course of the program, you develop a full AI-powered application, from LLM microservice to containerized, monitored production deployment. You leave with a portfolio piece that shows exactly what you can build.

Mentors From the Industry's Front Lines

Your instructors are senior engineers currently building AI systems at companies like Salesforce, Amdocs, Siemens, and Cognyte. They know what engineering teams need, because they’re the ones leading those teams.

Vetted Peers, Built-In Network

Admission is selective by design. Every student is screened for technical background, which means the engineers you learn alongside are the professionals you’ll want to know five years from now.

Learn From Engineers Still Doing the Work

Nikita_Golovko
Nikita Golovko
Principal AI Architect

Principal AI Architect at Siemens ($238B+) building critical AI platforms and software in the BigTech ecosystem. Former CTO and Head of R&D, engineered large distributed systems and trading platforms. A PhD and featured speaker at the AI Infrastructure Summit, specializes in production-grade architecture.

Daniel_Gotliv
Daniel Gotlieb
Director of AI Engineering

Director of AI Engineering at Trigo ($1B+), leading all AI initiatives and transforming retail technology. Scaled SciPlay from startup through a $2B IPO, managing 11 engineers. Trained hundreds of engineers and architected AI systems for millions of users.

Michael-Winer
Michael Winer
Principal AI Engineer

Leads end-to-end AI systems development at Yess (acquired by Amdocs), specializing in AI systems architecture, model development, and production deployment. Previously Data Science Lead at AppsFlyer and Data Science & BI Team Lead at Fyber. Brings extensive experience teaching and training tech professionals.

Lee_Blum
Lee Blum
Lead Software Architect

Designing next-gen security platforms protecting 1,000+ organizations globally. 20+ years architecting petabyte-scale systems for Goldman Sachs, Morgan Stanley, and government agencies. Speaker at O'Reilly Strata, champions practical AI integration in enterprise architecture.

Eviatar-Levy-768x768
Eviatar Levi
Founder and AI Architect

Leading AI Architecture at ProciGen.AI, an AI-First company where every Architect orchestrates dozens of agents. Specializes in Production AI, Observability, Evaluation, and Optimization for GenAI and Agentic AI systems. Previously led AI and LLM solutions at HPE and facilitated GenAI workshops.

Alek_kozanov
Alex Kuznetsov
AI Engineering & Automation Lead

AI Engineering & Automation Lead at AudioCodes, a NASDAQ-listed voice and UC technology company, bringing AI and automation into global service delivery. Previously CTO at WaveClear, building AI-first systems from the ground up, and led AI Engineering and DevOps at Ceva, the global chip giant. Trained hundreds of tech professionals.

Yaara_Cohen-768x768
Yaara Cohen
Engineering Manager, Full-Stack & AI

Engineering Manager at Handshaik operating in the business automation space. Has 15 years of experience in software development and R&D leadership, leading AI projects from concept to delivery. Specializes in LLM and agent-based systems, and teaches cohorts and lectures in the field. Previously held Senior and Tech Lead roles at Wix, BigPanda, and other companies.

Nikita_Golovko
Nikita Golovko
Principal AI Architect
Daniel_Gotliv
Daniel Gotlieb
Director of AI Engineering
Michael-Winer
Michael Winer
Principal AI Engineer
Lee_Blum
Lee Blum
Lead Software Architect
Alek_kozanov
Alex Kuznetsov
AI Engineering & Automation Lead
Eviatar-Levy-768x768
Eviatar Levi
Founder and AI Architect
Yaara_Cohen-768x768
Yaara Cohen
Engineering Manager, Full-Stack & AI

Sound good?

Leave your details and we'll get back to you

From Developer to AI Engineer

AI System Architecture

Translating Business Requirements into AI System Architecture, System Boundaries, Components, Interfaces, and Data Flows, Workflow vs. Agent Architecture, RAG, Structured Data, and Tool Integration Decisions, State, Memory, Context, and Orchestration Design, Reliability, Security, Observability, Evaluation, Latency, and Cost

1
Design-Driven Development

Defining System Requirements, Constraints, and Acceptance Criteria, Designing Components and Interfaces Before Implementation, Turning Architectural Decisions into Clear Implementation Plans, Aligning Development with the Intended System Design, Verifying Implementations Against Architecture, Requirements, and Expected Behavior

2
Coding Agents and Project Configuration

Using coding agents as disciplined engineering collaborators, Structured investigation prompts for understanding and modifying codebases, Project-level instruction files and configuration, Reusable skills, subagents, and hooks, Large-codebase navigation, debugging, and refactoring, Test generation and code review, Controlled permissions, context management, and repeatable agent workflows

3
LLM APIs and Model Integration

Moving from user-interface prompts to real software integrations, Raw HTTP requests and SDK abstractions, Authentication, message roles, and request structure, Public-cloud model APIs and streaming responses, Token accounting, latency, and cost awareness, Comparing provider SDKs and model behavior, Isolating provider-specific integrations behind replaceable adapters

4
Prompt Engineering and Model Behavior

Understanding the controls that shape model behavior, System prompting and role prompting, Instruction hierarchy and structured prompt patterns, Few-shot prompting techniques, Temperature, top-p, and token budgets, Controlled experimentation with model behavior

5
Context Engineering and Prompt Architecture

Treating the context window as a limited engineering resource, Context budgets and conversation history management, Summarization and context compaction, Progressive disclosure of information, Dynamic context assembly from user, task, business, and retrieval signals, Prompt caching and deterministic context ordering, Separating application state from model context

6
RAG Architecture and Document Ingestion

Building the complete retrieval-augmented generation pipeline, Loading and parsing enterprise documents, Fixed, recursive, sentence-aware, semantic, and overlapping chunking strategies, Embedding, storing, and retrieving document content, Context assembly and grounded generation, Citation-aware answers, Explicit handling of missing or conflicting information

7
Advanced Retrieval and Reranking

Improving retrieval quality beyond basic vector similarity, Metadata filtering, Hybrid lexical and semantic search, Reciprocal rank fusion, Query rewriting, Wide retrieval followed by relevance reranking, Quality, latency, cost, and complexity trade-offs

8
Tool Use and Function Calling

Giving models controlled access to data, actions, and external systems, Tool schemas, descriptions, selection, and dispatch, The complete tool-use execution round trip, Typed arguments and input validation, Tool errors, retries, and safe result handling, Trustworthy reporting of completed, blocked, and recommended actions

9
Agent Loops and ReAct

Understanding the control loop that turns model calls into agents, Plan, act, observe, and revise patterns, Tool use and external feedback, Stop conditions and turn budgets, Recovery paths and bounded autonomy, Tool-choice quality and loop efficiency, Failure modes including silent looping and confident failure

10
Stateful Workflow Orchestration

Building production-style agentic workflows with explicit execution control, LangGraph typed state, nodes, and edges, Conditional transitions and checkpointing, Choosing between deterministic workflows, finite-state systems, and autonomous agent loops, Approval gates, interrupts, and resumable execution, Explicit action status and safe continuation after approval or rejection, Fallback paths, termination conditions, and failure recovery

11
Model Context Protocol (MCP)

Connecting reusable tools through a standard client-server protocol, MCP server and client architecture, Capability discovery and tool invocation, FastMCP and typed contracts, Transports and authentication considerations, Retries and error handling, Connecting agents, coding environments, databases, APIs, and enterprise capabilities

12
Multi-Agent Patterns

Understanding when specialist agents provide more value than coordination cost, Supervisor, router, specialist, delegation, and handoff patterns, Context isolation between agents, Shared state and result aggregation, Cross-agent contracts, Reliability, latency, observability, and cost trade-offs

13
Multimodal AI Applications

Combining multiple input and interaction modes in AI applications, Text-based interaction patterns, Visual input integration, Audio input integration, Multimodal application architecture, User experience, quality, latency, and cost considerations

14
AI Evaluation and Regression Testing

Measuring semantic quality beyond traditional software testing, Golden datasets and reference-based metrics, Structured evaluation rubrics, LLM-as-judge evaluations, Evaluating tool calls and workflow behavior, Measuring groundedness, relevance, and safety, Offline evaluation, production sampling, baselines, and regression checks

15
AI Security and Guardrails

Treating the model as an untrusted reasoning component rather than a security boundary, Direct and indirect prompt injection, Data leakage, tool abuse, and confused-deputy risks, Permission-aware retrieval and least-privilege access, PII controls and fail-closed access policies, Guardrails, abstention, and grounding requirements, Approval gates and safe action boundaries

16
Containers and Public-Cloud Deployment

Packaging AI systems for repeatable deployment, Containerization with Docker, Configuration, secrets, and dependency management, Reproducible application builds, Public-cloud deployment architecture, Identity and access management with least-privilege model access, Operational verification after deployment

17

Upcoming training programs

Early
Registration
Discount Available
Daniel_Gotliv
United Kingdom - London
January 12th 2027 - April 20th 2027 (Tuesdays)
18:00 to 21:00 Central European Time

Instructor: Daniel Gotlib, Director of AI Engineering at Trigo

APPLY NOW
12
JAN
Yaara_Cohen-768x768
United States - New York
January 19th 2027 - May 4th 2027 (Tuesdays)
18:00-21:00 Eastern Standard Time

Instructor: Yaara Cohen, Engineering Manager at Handshaik

APPLY NOW
19
JAN
What are the admission requirements
All applicants need at least two years of professional engineering experience in a tech company, working knowledge of at least one backend language (Python preferred), and familiarity with APIs and version control. Candidates go through a screening and evaluation process before admission is confirmed. Upon acceptance, students receive access to a recorded Python foundations course to close any gaps before the program begins.
Is this a course on using AI tools, or on building AI systems
Building. Every module is focused on how AI systems actually work and how to engineer them for production. That means RAG pipelines, LLM integration, agent architecture, MCP server/client patterns, infrastructure, deployment, monitoring, and compliance. Not prompting tools or wrappers that abstract the engineering away.
How practical is the program?
Entirely practical. Every topic is taught through hands-on exercises and applied directly to a rolling project you build throughout the course. By the time the program ends, you'll have developed and deployed a complete AI-powered application, from LLM microservice through containerized, monitored production deployment, that you can bring into any technical interview or use as a foundation for your next project at work.
How is the program structured?
The program runs for 15 weeks, with weekly 3-hour sessions focused on real engineering challenges. Plan for additional time each week for project work, exercises, and self-directed learning. You'll have lifetime access to course materials and recordings, updated as the field evolves.
Who are the instructors?
Senior engineers currently working in AI and engineering leadership at companies including Salesforce, Microsoft, Siemens, Cognyte, and others. Selected based on hands-on experience building production AI systems and a track record of technical leadership. Many of them also interview and hire engineers at their companies, which means they know exactly what the market is looking for.
What kind of payment plans and options do you provide?
We offer flexible payment plans with up to 4 installments of €735 each. Limited scholarships and promotional discounts may be available for qualifying candidates. Contact our admissions team for more details.

Ready to level up your career?

Leave your details below and we'll get back to you.

Apply For The Upcoming Class

Leave your details below and we’ll get back to you.

Apply For The Upcoming Class

Leave your details below and we’ll get back to you.