Deesha Tech Academy
Menu

Deesha AI · PathwayP-04

AI Application Engineer

Ship AI features with retrieval, tools, agents and evaluation inside a real application.

Length
8–10 weeks
Courses
4
SkillLabs
8
Mode
Online / Hybrid
Fee
On request
Reserve a place

Where you can join

4 ways in, at Spring AI Developer, Python AI Developer, RAG Engineering Lab or Agent Builder Lab. Dates open shortly.

Tell us where you are
New · Deesha AI

The AI line is open.

The route

How you get there

6 groups over 8–10 weeks, and 3 of them are places you can join. You leave each one able to do the thing it names, not having sat through it.

  1. 01 · AI Application Development4 weeksJoin here

    Spring AI DeveloperorPython AI Developer

    Ask about Spring AI Developer · Start in Java if Spring Boot is home ground.

    Ask about Python AI Developer · Start in Python if that is where you build.

    Learn how modern AI applications are designed and build them in your existing backend stack: LLMs, prompts, structured outputs, streaming, memory, tool calling and application APIs.

  2. 02 · Retrieval & Knowledge SystemsJoin here

    RAG Engineering Lab

    Ask about RAG Engineering Lab · Two days on retrieval, if you already build with LLM APIs.

    Build AI applications that answer from trusted business data: documents, chunking, embeddings, retrieval, reranking, grounded answers, citations.

  3. 03 · AI Integration & Tooling

    MCP Tooling Lab

    Connect AI to APIs, databases, services and business systems through tools and MCP, safely retrieving information and performing actions.

  4. 04 · Agentic AI Development4 weeksJoin here

    Agent Builder LabAgentic AI Engineering (Java)Agentic AI Engineering (Python)

    Ask about Agent Builder Lab · Two days on agents, if your AI features already work and you want them to act.

    Move from AI-powered features to systems that perform multi-step work: the two-day Agent Builder Lab, then agentic engineering in the stack you chose in stage one.

  5. 05 · AI Quality & Reliability

    AI Reliability Lab

    Make AI behaviour measurable, controlled and dependable: evals, failure testing, guardrails, security, tracing, cost and latency you can read.

  6. 06 · Production AI Engineering

    Docker & KubernetesLogging & MonitoringCloud DeploymentProduction Agent Lab

    Take AI applications and agents from working demos to real production systems: containerise, deploy, persist state, handle failures, observe, scale, operate.

What you finish with

A deployed AI application with retrieval, tool calling, memory, evaluation gates and observability, built in your chosen stack and defended like a production system.

What the cohort includes

  • Cohort-paced structure with milestones, not a self-paced video library
  • Mentorship from practitioner trainers who build with these tools daily
  • Code review on your own work, every sprint
  • Graded capstone with individual feedback
  • Verified Deesha certification

Who teaches this

Learn with experienced practitioners

All trainers and mentors

Courses on this pathway are taught by these practitioners. Each batch names its own trainer before you enrol.

  • Vishal Shah

    Vishal Shah

    Founder & Principal Trainer

    Two decades building and teaching commerce, banking and cloud platforms, and still writing code

    • Java & Spring Boot microservices
    • TypeScript, React, Angular & Next.js
    • Composable commerce (commercetools)
    Full profile →
  • Shrenik Shah

    Shrenik Shah

    Principal Trainer

    Cloud and AI architect who has upskilled over 5,000 engineers in Java, React, Python and cloud

    • Python: Django, Flask, GenAI & agentic workflows
    • Java & Spring Boot
    • React & Angular, micro-frontend architecture
    Full profile →

Designed for

  • Working developers moving into AI application engineering
  • Java and Python backend engineers being asked for AI features
  • Final-year and advanced learners with real projects behind them

About this pathway

Every backend developer is about to be asked for an AI feature, and most courses answer with a chatbot notebook. This pathway answers with engineering: the full stack around modern language models (grounding, tooling, evaluation and agents) built inside real applications in the language you already work in.

Choose your stack once

The route begins where you already are: the same AI engineering journey, taught through Spring AI or through Python. You choose once, in stage one (and again at the agentic stage, in the same stack), and everything between applies to both.

Courses where depth lives, SkillLabs for the gaps

Two stages earn full courses: the application stack and agentic engineering. Everything else (retrieval, integration and tooling, reliability, production) is a focused two-day SkillLab, because those are capabilities you practise once properly and then apply, not bodies of knowledge to sit through.

Who this is for

You need real programming behind you: working Python for the Python branch, working Spring Boot for the Java branch. No AI background is assumed anywhere in the pathway; judgment about AI is the product, not the prerequisite.

Cohorts and fees

Cohorts are small so that every capstone gets individually reviewed. Fees and the next start dates are on this page, or message us on WhatsApp and we will help you choose the entry point that fits.

Reserve a place

Tell us where you are now. We will confirm the intake, the fee and whether this route fits.

This website does not store or send these details. Your email or WhatsApp app will open with the message ready for you to review and send.

Your portfolio when you finish

Design, build, ground, integrate, evaluate, deploy and operate secure, observable and resilient AI applications and agents for real users.

  1. 01

    An AI feature shipped inside a real application

    Structured outputs, streaming, tool calling, failure handling and cost controls: a feature users can touch, not a notebook demo.

  2. 02

    A knowledge assistant that cites its sources

    RAG over your own documents with measured retrieval quality, honest citations, and a refusal when the evidence is not there.

  3. 03

    An AI application wired into real systems

    Tools and MCP connecting your application to APIs, databases and business systems, retrieving and acting within explicit boundaries.

  4. 04

    A tool-using agent with real boundaries

    A goal, tools, a budget and a stop condition, plus the written argument for when an agent is the wrong answer.

More pathways

Pick where you want to end up

Browse all pathways

Full-Stack Developer (Java)

16–18 weeks · 8 courses · 11 SkillLabs

Intakes open regularly

See the route

Backend Developer (Java)

13–15 weeks · 6 courses · 10 SkillLabs

Intakes open regularly

See the route

Frontend Application Engineering

7–9 weeks · 4 courses · 6 SkillLabs

Intakes open regularly

See the route

Idea to Production

3 weeks · 0 courses · 3 SkillLabs

Intakes open regularly

See the route

P-04 · 8–10 weeks

Not sure this is the right route?

Tell us where you are now and we will say plainly whether this pathway fits, or which one does.

Reserve a place