AI Engineer — Java Stack (Spring AI & LangChain4j)
capability building,
designed for your organisation.
A custom-built corporate programme for Mid-to-senior Java developers (4+ years) embedding AI capabilities into existing Spring Boot, Jakarta EE, and reactive enterprise systems. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.
A modular syllabus, built to be tailored.
Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements — module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.
- Frontier model mental model: tokens, context windows, tool use, structured outputs — for Java devs who haven't lived this
- Calling OpenAI / Azure OpenAI / Anthropic from plain Java: streaming, retries, error handling
- When to use which provider; cost/quality/latency table for Java-stack workloads
- Lab: call Azure OpenAI from plain Java with proper streaming and retries
Want the full module-by-module syllabus, sample assignments, and pricing?
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Demonstrable skills your team will apply on live projects.
Build production-grade Spring AI / LangChain4j services
Streaming, structured outputs, tool calling, advisor patterns — production-ready from day one in Spring Boot 3.x.
Engineer enterprise RAG pipelines on the JVM
100k-doc corpus ingestion via Spring Batch + Reactor, vector backends (pgvector, Redis, Azure AI Search), hybrid retrieval with re-ranking.
Integrate AI into Kafka, Camunda, and legacy Java systems
Event-driven AI decisioning, BPMN with AI-task nodes, and patterns for embedding LLMs in established Java estates.
Pass the joint capstone evaluation panel
Each engineer ships a production-grade Spring Boot AI service with citations, observability, guardrails, and a cost/latency report.
Earn the AI Engineer (Java) credential
Cohort first-attempt completion rate of 91%. Two attempts permitted. Built explicitly for services-firm and BFSI delivery roles.
Lead AI delivery on Java-heavy client accounts
Java-stack AI engineers are the scarcest profile in 2026 enterprise delivery. Alumni typically take an immediate scope leap on BFSI and telco engagements.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Strong Java (17+) and Spring Boot, but no production LLM integration experience
- ·Comfortable with REST, Maven/Gradle, JUnit; new to AI/ML production patterns
- ·Have called OpenAI from Java but haven't built real RAG, agents, or grounded retrieval
- ·Limited fluency with Spring AI's ChatClient, Advisors, and EmbeddingClient
- ·No experience integrating LLMs with Kafka-driven workflows or BPMN/Camunda flows
- ·Cannot independently apply JVM-specific patterns (Project Reactor, virtual threads) to AI workloads
Where they'll arrive
- ✓RAG pipeline engineering on the JVM — production-grade retrieval pipelines on the Java stack
- ✓Agentic AI workflow development — tool-using stateful agents with Spring AI and LangChain4j
- ✓AI-augmented enterprise automation — plugging LLMs into Spring Boot microservices and Kafka flows
- ✓JVM-specific performance patterns — Project Reactor, virtual threads, async, batching for AI
- ✓Production observability — Micrometer + OpenTelemetry + LLM tracing (Langfuse, Arize)
- ✓Cost & latency tuning — caching, routing, model downgrade, semantic cache on the Java stack
Built for L&D outcomes, not seat counts.
Prompt discipline, not prompt luck
Learners move from trial-and-error prompting to named patterns such as role prompting, few-shot, prompt chaining, and self-critique.
Reusable team assets
The programme produces Custom GPTs, reusable workflow templates, and a shared prompt library that teams can govern and scale.
Daily productivity workflows
Labs focus on email, reports, slides, meetings, spreadsheets, research synthesis, and role-based business assignments.
Measured time savings
Capstone workflows document recurring task compression, review-cycle reduction, and before/after productivity improvements.
Responsible enterprise use
Learners practise confidentiality, IP, bias detection, verification checklists, and safe-use protocols before adoption at scale.
Sustainment built in
30-day, 60-day, and 90-day check-ins help learners keep pace as ChatGPT features and frontier models evolve.
A four-milestone path from skill gap to client-ready.
Foundation & baseline
Establish a working mental model of ChatGPT, frontier models, tokens, context windows, hallucination risks, and model-selection trade-offs.
Prompt engineering labs
Learners practise CRISPE, SPEAR, role prompting, constraint-led prompting, few-shot prompting, self-critique, and prompt iteration on real work scenarios.
Custom GPTs & workflow automation
Each learner builds reusable GPTs and connects ChatGPT to productivity tools for email, documents, spreadsheets, meetings, and research workflows.
Capstone & sustainment
Learners demonstrate a personal AI productivity system and continue with prompt-of-the-week, model-of-the-month, and 30/60/90-day check-ins.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Domain-expert trainers, not professional presenters.
"My job isn't to teach ChatGPT as a tool — it's to help professionals build repeatable AI workflows, verify the output, and reclaim hours from routine work."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·Knowledge workers who want to apply ChatGPT productively in their daily workflows
- ·Business analysts, consultants, marketing professionals, project managers, and individual contributors
- ·Teams that use ChatGPT for occasional drafting but need reliable, business-grade outputs
- ·Managers looking to establish team-wide prompt standards and safe-use protocols
- ·Organisations that want to automate repetitive work across email, spreadsheets, calendars, and documents
Pre-requisites
- ·No coding prerequisite for business and productivity tracks
- ·Basic familiarity with workplace tools such as email, documents, spreadsheets, slides, and meetings
- ·Willingness to bring real recurring tasks into labs for workflow redesign
- ·Enterprise cohorts should align data-handling expectations before learners use company or client information
Trusted by L&D leaders across the world.
"The programme moved our team from random prompting to a repeatable method. The prompt library and Custom GPTs became assets we could actually reuse."
"The most useful part was workflow automation. Learners took their weekly reports, meeting recaps, and research tasks and reduced hours of repetitive effort."
"Responsible use was handled practically. The team finally understood what can be pasted, what must be masked, and how to verify output before sending it."
Questions L&D teams ask before signing.
No, the course is designed for Java and Spring developers who want to build AI applications without switching to a Python-first stack.