Senior AI-Native Java Engineer & Architect Program
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SOFTWARE DEVELOPMENT · ADVANCED LEVEL

Senior AI-Native Java Engineer to Software & AI Solutions Architect

A 6-month advanced program to master microservices, Kubernetes, distributed systems, and production AI features like RAG, MCP, and autonomous agents in Java backends.

Advanced24 weeks192 hoursHybrid
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COURSE HIGHLIGHTS

What makes this program worth your time.

🚀

Production-first curriculum

Every topic is taught through the lens of shipping and operating real production systems, not just tutorials.

🧠

AI integration as a core skill

LLM APIs, RAG, MCP, and agents are taught as standard backend engineering practices, fully integrated with Spring Boot.

🛠️

Deep hands-on labs

Weekly labs covering Kafka, Kubernetes, resilience patterns, and vector databases build real muscle memory.

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YOUR LEVEL
BeginnerIntermediateAdvancedExpert
WHAT YOU WILL BE ABLE TO DO

The skills you will have by the end of this course.

  • Design and implement production-grade microservices with advanced Spring Boot, Hibernate, and JPA techniques
  • Secure REST APIs using modern authentication and authorization standards (OAuth2, JWT)
  • Architect event-driven systems using Kafka and apply caching and resilience patterns for high-traffic services
  • Containerize applications, automate CI/CD pipelines, and deploy to Kubernetes with observability tooling
  • Integrate LLM APIs into Java backends and build a RAG pipeline using embeddings and a vector database
  • Implement tool calling, MCP integrations, and multi-step AI agents to automate backend tasks
  • Evaluate and optimize AI systems for cost, latency, and reliability
  • Ship a complete AI-native, production-ready microservices platform as a capstone project
CONDENSED SYLLABUS

See the structure without reading a textbook.

Modules stay collapsed for quick scanning. Open any module to inspect its topics.

01Advanced Build Tools and Git for Teams5 lessons+

Advanced Maven and Gradle: multi-module projects and custom plugins

Dependency management strategies and reproducible builds

Advanced Git workflows: rebasing, cherry-picking, and monorepos

Branching strategies for teams: trunk-based development vs GitFlow

Code review practices and pull request hygiene

02Advanced Spring Boot and API Security5 lessons+

Spring Boot internals: auto-configuration and custom starters

Securing REST APIs with Spring Security, OAuth2, and JWT

Role-based and attribute-based access control

API rate limiting and input validation at scale

Testing strategies: contract testing and integration test slices

03Advanced Hibernate and JPA Performance5 lessons+

Entity relationships, fetch strategies, and the N+1 problem

Second-level caching and query optimization

Transaction management and isolation levels in practice

Auditing, versioning, and schema migration with Flyway/Liquibase

Profiling and tuning Hibernate for high-throughput services

04Microservices Architecture and Event-Driven Systems5 lessons+

Microservices design principles and bounded contexts

Event-driven communication fundamentals with Apache Kafka

Designing topics, partitions, and consumer groups

Event sourcing and CQRS patterns

Service discovery and API gateway patterns

05Containerization, CI/CD, and Kubernetes5 lessons+

Advanced Docker: multi-stage builds and image optimization

Building robust CI/CD pipelines for microservices

Kubernetes fundamentals: pods, deployments, and services

Configuring health checks, autoscaling, and rolling updates

Observability on Kubernetes: logging, metrics, and tracing

06Distributed Systems Resilience5 lessons+

Caching patterns for high-traffic systems (local, distributed, write-through)

Circuit breakers, retries, and bulkheads with Resilience4j

Handling distributed consistency and idempotency

Load balancing and backpressure strategies

Chaos engineering basics for production readiness

07Integrating LLM APIs into Java Backends5 lessons+

LLM API fundamentals: prompts, tokens, and streaming responses

Designing Java services that wrap and orchestrate LLM calls

Handling reliability, retries, and fallback strategies for AI calls

Structuring prompts and outputs for backend consumption

Cost and latency considerations when calling LLM APIs

08Building RAG Pipelines with Vector Databases5 lessons+

Embeddings fundamentals and choosing an embedding model

Storing and querying vectors with a vector database

Designing a retrieval-augmented generation pipeline in Java

Chunking strategies and relevance tuning

Evaluating RAG output quality and reducing hallucinations

09AI Agents, Tool Calling, and MCP5 lessons+

Tool calling fundamentals: exposing backend functions to LLMs

Introduction to the Model Context Protocol (MCP) for tool integration

Building simple single-step AI agents in Java

Designing multi-step agents with planning and execution loops

Building evaluation pipelines to test agent reliability

10Capstone: Production-Ready AI-Native Platform6 lessons+

Architecting an end-to-end AI-native microservices platform

Implementing security, caching, and resilience across services

Deploying the platform to Kubernetes with full observability

Integrating RAG and agent-based features into the platform

Optimizing the system for cost, latency, and reliability

Final project presentation and architecture review

UPCOMING GROUPS

Choose the cohort you can actually attend.

Only current, open groups are shown.

STARTS

To be announced

Hybrid
Schedule
To be announced
Format
Hybrid
Duration
24 weeks · 192 hours
Language
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CONTEXTUAL PROOF
“The program helped me connect individual skills into the way real teams design, build and deliver software.”

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APPLICATION THROUGH INGRESS PORTAL

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We use one Portal account for applications, assessments and future learning progress. You will not need to email your details or select the training again.

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  1. 01

    Create or sign in to your Portal accountYour contact details stay connected to one student profile.

  2. 02

    Confirm your application detailsSenior AI-Native Java Engineer to Software & AI Solutions Architect are preselected.

  3. 03

    Submit your applicationThe admissions team receives it immediately and can follow up from the Portal.

SELECTED TRAININGSenior AI-Native Java Engineer to Software & AI Solutions ArchitectHybrid

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