Skip to content Free Sunday sessions · Sun 30 Aug · Online · Register now →
Enquire Here
Intraneurals Infotech Pvt Ltd · Tiruvallur & Chennai

AI EngineerCareer Launch Programme

Six months. Build an AI product that is deployed, running and yours to explain.

A project-led programme for students and recent graduates who want to build modern AI applications using language models, retrieval, agents, MCP, deployment, evaluation and production engineering practices.

Enquire Here Download Brochure View Curriculum ↓

What you will have at the end of six months

The programme is built around work an employer can inspect, run and question you about.

A

A live AI product

A deployed application that can be opened and tested through a real URL.

B

A six-month GitHub history

A repository that shows how the system developed week by week.

C

A technical design document

A clear explanation of the architecture, decisions, alternatives considered and engineering trade-offs.

D

A technical walkthrough

A recorded ten-minute explanation of your system, architecture, important decisions and results.

No two students in a batch complete the same capstone project. Students choose a problem early in the programme and progressively build it as they learn new engineering concepts.

Roles this programme prepares you for

The skills in the programme map to entry-level roles including:

Job titles vary between companies. The programme focuses on the engineering capabilities behind these roles rather than training for one particular job title.

Is this programme right for you?

Good fit if:

  • You are in your pre-final year, final year or have graduated recently.
  • Students from different engineering and technical branches may apply.
  • You can already write basic Python using loops, functions and files.
  • You want to build actual AI applications rather than study only theory.
  • You can commit consistently throughout the six-month programme.
  • You are comfortable learning by building, debugging and improving your own work.

If a student does not yet have the required Python foundation: Ask us about the Python for AI bridge programme before joining.

This programme may not suit you if:

  • You are looking only for a certificate.
  • You want only classroom theory.
  • You want to complete only predefined dummy projects.
  • You expect every solution to be provided step by step without building independently.

You will receive guidance, code reviews, project support and remediation throughout the programme, but the work you present at the end must genuinely be yours.

How the six months are structured

Months 1–2 · Foundations

60 hours

Students establish the engineering foundation required to work with modern AI systems and begin their individual capstone.

  • Python for AI engineering
  • modern AI landscape
  • transformers, tokens, embeddings and attention
  • prompt engineering
  • LLM engineering
  • OpenAI SDK
  • Anthropic SDK
  • Gemini API
  • embeddings
  • vector search

Students select their capstone problem during this period.

Months 3–4 · AI Systems

72 hours

Students learn how modern AI applications retrieve information, use tools, maintain state and interact with external systems.

  • Retrieval Augmented Generation
  • LangChain
  • LlamaIndex
  • AI agents
  • LangGraph
  • CrewAI
  • multi-agent systems
  • Model Context Protocol
  • MCP Python SDK
  • MCP Inspector
  • FastAPI
  • deployment
  • Docker
  • GitHub Actions

Each major concept is applied to the student's own project.

Months 5–6 · Production Engineering

48 hours

Students take the working system closer to the standard expected of real software.

  • evaluation
  • regression testing
  • observability
  • tracing
  • logging
  • latency
  • token economics
  • security
  • prompt injection
  • jailbreak resistance
  • data leakage
  • responsible AI
  • Indian data protection obligations
  • production hardening
  • documentation
  • technical presentation
  • portfolio preparation
  • technical interview preparation

180 contact hours in total. Students should plan additional project and practice time outside contact hours. A realistic total commitment is approximately 11–13 hours per week, depending on the student's pace and project.

Learn through smaller builds. Prove it through one serious project.

The course uses two complementary forms of project work.

Engineering mini-builds

During individual modules students complete short practical builds such as:

These are designed to make each technical concept concrete before it enters the capstone.

Individual capstone

Each student also develops one substantial AI application over the full six months. The capstone progressively incorporates application logic, model APIs, retrieval, tools, agents where appropriate, databases, evaluation, monitoring, deployment, security and documentation.

The mini-builds teach the individual engineering skills. The capstone shows that you can combine them into a working system.

How progress is assessed

Month 1 — Foundations check

Python, APIs, data handling and core software skills.

Month 2 — Independent build

A small working AI application built with limited assistance.

Month 4 — Engineering assessment

Students build a small working system from an empty project and complete a debugging task involving unfamiliar code. Part of this assessment is completed without AI assistance so that students and instructors can identify whether the underlying engineering skills are strong enough. Students who need additional support enter a structured remediation period before moving forward.

Month 6 — Capstone review

The final application is evaluated on whether it works, architecture, code quality, evaluation, reliability, deployment, documentation and technical explanation.

Full curriculum

The complete technology stack is preserved below. Expand each module for topics and hours.

MONTHS 1–2 · FOUNDATIONS

Python for AI engineering 12 hours
object-oriented programming typing async programming environments packaging APIs JSON file handling error handling debugging basics

This assumes basic Python knowledge and develops the Python required for AI application engineering.

Modern AI landscape 10 hours
transformers tokens embeddings attention context model behaviour model limitations cost latency selecting models based on application requirements
Prompt engineering 14 hours
structured prompting structured output tool calling prompt templates prompt versioning reasoning workflows systematic prompt evaluation guardrails
LLM engineering 16 hours
OpenAI SDK Anthropic SDK Gemini API streaming function calling structured outputs guardrails tokens cost control rate limits local models Ollama
Embeddings and vector search 8 hours
embeddings similarity search indexing metadata search hybrid search ChromaDB FAISS Pinecone

MONTHS 3–4 · AI SYSTEMS

Retrieval Augmented Generation 20 hours
document ingestion document parsing chunking embeddings retrieval reranking context construction memory hallucination reduction citation evaluation

Build with: LangChain · LlamaIndex

AI Agents 20 hours
agent architecture planning tool use state memory reflection multi-step workflows human-in-the-loop design reliability failure handling

Build with: LangGraph · CrewAI

Multi-agent systems 8 hours
specialised agents delegation collaboration orchestration state management event-driven workflows communication between agents understanding when a single-agent or conventional software architecture is preferable
Model Context Protocol 12 hours
MCP architecture servers clients resources tools transport authentication security file access database access workflow integration

Build with: MCP Python SDK · MCP Inspector

Deployment and software delivery 12 hours
FastAPI APIs and HTTP application structure environment variables deployment Docker Git GitHub GitHub Actions CI/CD cloud deployment basic authentication and access-control patterns

Each student's capstone must be deployed.

MONTHS 5–6 · PRODUCTION ENGINEERING

Evaluation and testing 12 hours
evaluation datasets qualitative evaluation quantitative evaluation regression testing testing retrieval testing prompts measuring system changes comparing versions unit and integration testing where appropriate
Observability, reliability and cost 10 hours
tracing logging monitoring latency failures model usage token economics cost analysis debugging production behaviour
Security and responsible AI 10 hours
prompt injection jailbreaks data leakage unsafe tool access personal information access control OWASP guidance for LLM applications India's Digital Personal Data Protection framework human oversight safe failure
Capstone engineering 8 hours
code review architecture review evaluation reliability testing security review deployment review documentation technical design document
Interview and portfolio 8 hours
technical resume GitHub presentation portfolio presentation system design discussion explaining engineering decisions debugging questions mock technical interviews project walkthrough practice

Choose a problem you can understand deeply

Students select a domain and define a specific problem with guidance from the teaching team. Projects may come from a student's interests, internship exposure, family business, local industry problem or original idea. The examples below indicate the expected level of system design.

Healthcare

A medical knowledge assistant that works from approved reference material, retrieves and cites evidence, identifies unsupported questions, records feedback and measures retrieval quality. It is not designed to diagnose patients independently.

Education

A learning system that records student performance, identifies weak concepts, retrieves relevant learning material, adapts subsequent questions and provides teachers with evidence of student progress.

Human Resources

A recruitment support system that extracts candidate evidence, compares it against defined job requirements, identifies missing information and provides explainable recommendations for human review. Discriminatory automated hiring logic is not part of the programme.

Legal

A contract intelligence system that identifies obligations, dates, unusual clauses, defined terms and risks while linking every extracted point to its source text.

Finance

A financial research system that reads financial statements, filings and approved source documents, extracts relevant information, identifies changes and provides evidence-backed analysis with appropriate controls. It is not marketed as autonomous financial advice.

Retail

A catalogue-aware shopping system connected to structured product information that understands constraints, compares appropriate products, explains recommendations and handles changing inventory.

Manufacturing

An equipment-support system combining manuals, maintenance history and fault information to identify relevant evidence, guide troubleshooting, record technician feedback and evaluate retrieval accuracy.

Digital Marketing

A marketing workflow that prepares campaign assets, records variations, supports controlled testing, analyses campaign data and produces traceable reports for human approval.

Customer Support

A support system that retrieves approved answers, uses tools where authorised, identifies when it cannot safely resolve an issue, escalates correctly and records unresolved cases.

Enterprise Knowledge

A controlled knowledge system that retrieves information from distributed organisational documents, respects access rules, cites sources and measures answer quality.

Students are not restricted to these domains.

Tools and technologies

The programme distinguishes between technologies students build with regularly, technologies they use through hands-on exercises, and technologies they learn to compare and evaluate.

Build with

These receive deep, repeated use across classes, mini-builds and capstone work.

Language and model SDKs

  • Python
  • OpenAI SDK
  • Anthropic SDK
  • Gemini API

Frameworks

  • LangChain
  • LangGraph
  • CrewAI

Retrieval

  • ChromaDB

Protocol

  • MCP Python SDK
  • MCP Inspector

Application and deployment

  • FastAPI
  • Docker
  • Git
  • GitHub
  • GitHub Actions

Data

  • PostgreSQL

Work with

Students use these through labs, comparison exercises and selected project work.

Models

  • Claude
  • GPT
  • Gemini
  • Llama
  • Mistral
  • Qwen

Local and open models

  • Ollama
  • Hugging Face

Frameworks

  • LlamaIndex
  • Pydantic AI

Vector stores

  • FAISS
  • Pinecone

Interfaces

  • Streamlit
  • Gradio

Cloud

  • Render
  • Railway
  • Azure AI

Data

  • MongoDB

Observability

  • tracing tools for LLM applications
  • evaluation tools for LLM applications

Know and compare

These are demonstrated, discussed and compared so students understand where they fit in the current AI engineering ecosystem.

Frameworks and agent platforms

  • AutoGen
  • Haystack
  • OpenAI Agents SDK
  • Google ADK

Vector databases

  • Qdrant
  • Weaviate

Cloud AI platforms

  • AWS Bedrock
  • Google Vertex AI

Models

  • Grok
  • relevant frontier and open-model releases available during the programme

AI engineering changes quickly. Where a technology materially changes during a batch, the teaching team may update the relevant tool while preserving the engineering concept being taught.

Software engineering foundation

Visible across the curriculum rather than hidden behind frameworks:

Placement Support

Placement preparation is integrated into the latter part of the programme so that students are ready to present their work professionally when they begin applying.

Placement support is provided. Employment depends on the student's performance, hiring requirements and available opportunities and therefore cannot be guaranteed.

Who is behind the programme

Programme design

Siddharth T. Janakiraman

Co-founder, Intraneurals

Techno-legal background across intellectual property, technology commercialisation and regulatory practice. Works with founders, innovators and university-linked programmes on intellectual property, innovation and protection of technology products.

Selvaganesh J

Co-founder and Chief Executive, Intraneurals

Software delivery background including TCS and Capgemini, with enterprise project experience across large engineering teams.

Teaching team

Core technical modules are delivered by experienced faculty members and AI practitioners who currently work with AI and software systems in industry.

Industry mentors

Working engineers and industry practitioners contribute through selected technical sessions, project reviews, capstone feedback and mock interviews.

Programme Fee

Founding Batch

₹49,500

First cohort. Includes programme access and reasonable programme-related API and cloud usage during the six-month course. Usage is provided for normal course, lab and capstone requirements subject to the programme's reasonable-use policy.

Standard Fee

₹65,000

From subsequent batches.

Instalments

Instalment payment available. Contact admissions for the current schedule.

Enquire Here

A note for parents

When comparing technical programmes, ask what the student will actually be able to show an employer at the end.

An Intraneurals AI Engineer student is expected to finish with:

These outputs make it easier for a parent, teacher or employer to see what the student has actually learned.

For colleges and placement teams

The programme is designed around demonstrable engineering outcomes rather than attendance alone.

TPOs can evaluate students through:

Discuss a college cohort

Admission process

Enquire or apply

Apply through the website or contact Intraneurals through WhatsApp.

Basic entry check

A short assessment of basic Python, reasoning and readiness. Approximate duration: 40 minutes. It is used to identify whether the applicant is ready for the programme or would benefit from the Python bridge first.

Short programme conversation

A brief discussion about the student's background, goals, available time and potential capstone interests.

Confirm admission

Eligible applicants confirm their seat and receive onboarding instructions.

Batch size is capped at 25 to allow meaningful project supervision.

Next batch: [month to be announced] · Application closing date: [to be announced]

Build something you can explain because you built it.

Six months of structured AI engineering, smaller practical builds, one individual capstone and the technical preparation to present your work to employers.

Enquire Here Download Brochure
Intraneurals Infotech Pvt Ltd
Tiruvallur & Chennai, Tamil Nadu
admissions@intraneurals.com
WhatsApp: +91 98409 41910
www.intraneurals.com
Enquire Here