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
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Enterprise learning solutions built for corporate teams.
Go beyond standard classroom delivery with enterprise-ready learning infrastructure, managed execution, capability insights, and production-like practice environments designed for corporate scale.
Enterprise Command Center (LMS+)
Managed Batches (End-to-End Execution)
Capability Audits (Pre-Training Intel)
Custom Chaos Sandboxes
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.
Java-native AI engineering, not Python ported
This AI engineer corporate training for Java developers is built entirely on the JVM Spring AI, LangChain4j, Project Reactor, and virtual threads not a Python course retrofitted for Java teams.
Spring AI training course with production depth
Learners go deep into ChatClient anatomy, advisor patterns, EmbeddingClient, and structured outputs the Spring AI training course that takes engineers from API calls to production-grade services.
Spring AI corporate training on real enterprise stacks
Every lab runs on Spring Boot 3.x with Kafka, Camunda, pgvector, and Azure AI Search the Spring AI corporate training built for services-firm and BFSI delivery environments.
RAG pipelines and agentic workflows on the JVM
Engineers build 100k-document ingestion pipelines with Spring Batch, hybrid retrieval with re-ranking, and multi-step supervisor-worker agents all in Java.
Spring Boot AI certification through a joint capstone
Each engineer earns a Spring Boot AI certification by shipping a production-grade service with RAG, tool calling, observability, guardrails, and a cost/latency report evaluated by a joint industry panel.
AI training for Java Spring developers, adapted to your stack
Module depth, lab environments, and capstone scenarios are tailored per engagement this AI training for Java Spring developers is rebuilt around your tech stack and target project archetypes.
A four-milestone path from skill gap to client-ready.
LLM foundations and Java AI stack setup
Establish the LLM mental model for Java developers tokens, context windows, tool use, and structured outputs and wire the first Spring AI and LangChain4j services with streaming, retries, and error handling.
RAG pipelines, vector stores, and retrieval engineering
Build production-grade embeddings pipelines with Spring Batch and Project Reactor; integrate pgvector, Redis, and Azure AI Search; implement chunking strategies, hybrid retrieval, and cross-encoder re-ranking the core of this corporate AI upskilling program for Java teams.
Agents, enterprise automation, and performance
Implement multi-step tool-using agents with Spring AI and LangChain4j; integrate AI into Kafka event streams and Camunda BPMN flows; and apply this AI training for Java Spring developers to reactive performance patterns async, batching, and virtual threads under LLM load.
Observability, cost governance, and capstone
Wire Micrometer and OpenTelemetry across the full AI stack; apply cost and latency tuning; then ship the capstone a production Spring Boot AI service evaluated by a joint NovelVista AI practice and industry SME panel.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Trusted by Industry Leaders for Enterprise AI Upskilling
See why CEOs, CTOs, and business leaders collaborate with NovelVista
to discuss the future of AI, digital transformation, and workforce readiness.
- Exclusive AI leadership summits featuring enterprise decision-makers and technology experts
- Recognized corporate training partner for AI, Agile, DevOps, ITSM, and cybersecurity programs
- Trusted by organizations to build future-ready teams with practical, industry-focused learning
- Real conversations, real business challenges, and actionable AI transformation insights from industry leaders
Learn from domain experts with 15+ years of experience.
"My goal is not to teach Java developers how to call an LLM API it is to build engineers who can ship production-grade RAG services, agentic workflows, and observable AI systems on the Spring stack, ready for real client delivery from week one."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·Mid-to-senior Java developers (4+ years) on Spring Boot, Jakarta EE, and reactive enterprise systems who want to embed AI capabilities without switching to a Python-first stack
- ·Engineering teams enrolling in AI engineer corporate training for Java developers to close the gap between existing Java expertise and production LLM integration
- ·Backend architects and Java tech leads responsible for designing RAG pipelines, agentic workflows, and AI-augmented enterprise automation on the JVM
- ·Services-firm and BFSI delivery teams where Spring AI corporate training must map directly to client project archetypes and regulated-industry delivery standards
- ·L&D leaders building a Spring Boot AI certification pathway for Java engineering cohorts transitioning from classical backend development to AI-native delivery
Pre-requisites
- ·Strong hands-on Java experience (Java 17 or above) with Spring Boot this programme extends existing backend engineering skills into AI-native patterns
- ·Working familiarity with REST APIs, Maven or Gradle, and JUnit no prior machine learning or data science background is required
- ·Basic awareness of cloud platforms (Azure, AWS, or GCP) is helpful but not mandatory cloud concepts are introduced progressively through the labs
- ·Enterprise cohorts should confirm lab environment access (Spring Boot 3.x, Kubernetes or Docker, and a supported vector database) before the programme kick-off
Trusted by L&D leaders across the world.
"The AI engineer corporate training for Java developers gave our Spring Boot team a complete production playbook from RAG pipelines and vector store integration to agentic workflows and cost observability. Our first client AI service went live within three weeks of the capstone."
"The Spring AI training course depth was unlike anything else we evaluated. The advisor pattern labs and structured output sessions closed gaps our team had been working around for months. The capstone evaluation by the industry SME panel added real credibility."
"We needed a corporate AI upskilling program that worked on our actual stack Kafka, Camunda, and Spring Boot. Every lab environment matched our delivery context. The Kafka and BPMN AI integration modules alone justified the programme investment."
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.