IntraneuralsAI Engineer
A six-month course in applied AI engineering for people who want to build systems with language models, retrieval, agents and production practice, and be able to explain the decisions behind what they ship.
Applications close · Online and fully live · Small batches
Most introductions to generative AI stop at prompting, demos, or short projects that never leave a notebook. This programme is organised differently. You spend six months learning how contemporary AI applications are engineered: how models are called and constrained, how documents are retrieved and cited, how agents use tools, how systems are evaluated, deployed and kept reliable, and you apply each idea to one individual product that you own from the first weeks through final review.
The contact load is 180 supervised hours, with roughly another 110 hours of independent engineering on your own system. Sessions are online and fully live (not recorded self-paced content), and batches are kept small so review and remediation can stay specific rather than generic.
Open a section for the detail. Curriculum modules, tooling, domains and assessment criteria sit behind toggles so you can read at the depth you need.
Assessment is organised around artefacts an external reviewer can inspect, not around attendance or a certificate alone. By the end of the programme you should have four things that travel with you into interviews and further work.
No two students share the same capstone. You select a problem early, with guidance, and develop it as each engineering topic is introduced.
The programme is intended for pre-final and final-year students, recent graduates, and working professionals who already write Python and want a structured path into AI application engineering. Titles vary by employer (junior AI engineer, GenAI developer, RAG engineer, AI automation developer, and related software roles that touch language-model systems), so the curriculum focuses on the underlying capabilities rather than one job description.
Before joining you should be comfortable with basic Python (loops, functions, files, simple object orientation), Git and GitHub, API fundamentals, and the general shape of a web application. Applicants who are not yet there can prepare through the Python for AI bridge.
A good fit if you want to learn by building and debugging your own work, can sustain effort across six months, and are less interested in theory-only classrooms or predefined dummy projects. It is a poor fit if you mainly want a certificate, expect every solution to be handed down step by step, or cannot commit consistently to labs and independent build time.
If you already work as a developer, tester, support engineer or data engineer and use Python, the programme is structured for a career transition into AI engineering, not only for a first role after college. Live online sessions are scheduled so working professionals can attend. You complete the same individual capstone, code review and portfolio standards as every other participant; the work you present has to be yours.
A short preparatory path for applicants who need to reach the Python level required before the main programme. Duration and schedule depend on your starting point, so these are set individually during the admission conversation rather than fixed for everyone.
We publish taught contact hours separately from independent build time. Blending them into one figure hides how much of the programme is actually supervised.
Language and model SDKs
Frameworks
Vector databases
Protocol and delivery
Model providers
Cloud and hosting
Data and operations
Frameworks and platforms demonstrated for orientation: AutoGen, OpenAI Agents SDK, Google ADK, and relevant frontier or open-model releases during the batch.
Software foundations taught across the curriculum rather than hidden behind frameworks: Git, GitHub, APIs, HTTP, JSON, Poetry, UV, pip, typing, object-oriented Python, async programming, debugging, Docker, CI/CD, unit and integration testing, and authentication concepts.
Where a technology changes materially during a batch, the teaching team may update the tool while preserving the engineering concept being taught.
Fifteen modules across three phases, for 180 contact hours. Labs and mini-builds make each topic concrete as it is taught. One individual capstone runs through the full six months and is what you are assessed on; there is no separate layer of busywork projects.
Weekly live session days and timings are confirmed for each cohort during the admission conversation and shared before you enrol. See frequently asked questions.
Curriculum at a glance
Full description, labs and what each module builds toward are below. Open a phase to read it.
Establish the engineering foundation for modern AI systems and select the capstone problem.
Object-oriented programming, decorators, context managers, typing, async programming, virtual environments, Poetry, UV, pip, packaging, APIs, JSON handling, file handling, error handling and debugging. Assumes basic Python and develops the level needed for AI application work.
How contemporary AI systems are organised: foundation models, transformers, tokenization, embeddings, attention, context limits, model behaviour and failure modes, latency, and selecting models against application requirements rather than brand familiarity.
Prompt design, role and few-shot patterns, chain of thought, ReAct, chaining and templates, versioning, systematic evaluation, optimisation and guardrails, treated as engineering practice, not as improvisation.
OpenAI, Anthropic and Gemini SDKs; local models via Ollama and Hugging Face; streaming; function and tool calling; structured output; guardrails; token behaviour; rate limits; and model selection under real constraints.
Embeddings, similarity search, indexing concepts, metadata and hybrid search, with practical work across ChromaDB, FAISS, Pinecone, Qdrant and Weaviate.
How applications retrieve information, use tools, maintain state and connect to external systems.
RAG architecture, ingestion and parsing, chunking, embeddings pipelines, retrieval and reranking, hybrid search, context construction, hallucination reduction, citation, evaluation and optimisation. Built with LangChain, LlamaIndex and Haystack.
Agent architecture, planning, reflection, memory and state, tool use, multi-step workflows, human-in-the-loop patterns, evaluation and failure handling. Built with LangGraph, CrewAI and Pydantic AI; AutoGen, the OpenAI Agents SDK and Google ADK are demonstrated for comparison.
Specialised agents, communication, delegation and orchestration, including when a single-agent or conventional software design is the better choice.
MCP architecture: servers, clients, hosts, resources, prompts, tools, transport, authentication and security, with local and remote patterns for files, databases and workflow integration.
FastAPI, application structure, environment configuration, Streamlit and Gradio, Docker, GitHub Actions, CI/CD, cloud deployment (Render, Railway, Azure AI, AWS), and basic authentication patterns. Every capstone must be deployed.
Bring the working system closer to the standard expected of real software, and prepare to present it.
Evaluation datasets, qualitative and quantitative methods, regression testing for retrieval and prompts, measuring change across versions, unit and integration testing.
Tracing, logging, monitoring, latency, failure modes, model usage and token economics, and debugging production behaviour in LLM applications.
Prompt injection and jailbreak resistance, data leakage, unsafe tool access, PII and access control, OWASP guidance for LLM applications, governance, bias, copyright and IP considerations, India’s Digital Personal Data Protection framework, human oversight and safe failure.
Structured code and architecture review, evaluation, reliability, testing, security and deployment review, hardening and documentation against the technical design document.
Resume built from actual technical work, repository presentation, system design for LLM applications, coding and architecture interview practice (RAG, agents, MCP), debugging interviews, mock technical interviews and capstone presentation.
Progress is checked at intervals across the six months rather than deferred to a single end-of-course exam. This is a founding batch, so what we can show externally today is the assessment standard itself (published before enrolment) rather than a gallery of past graduate work.
You choose a domain you can understand deeply and define a specific problem with the teaching team. Ideas may come from your own interests, internship exposure, a family business, a local industry need, or an original proposal. The examples below indicate the expected level of system design; you are not limited to them.
Works from approved reference material, retrieves and cites evidence, identifies unsupported questions, records feedback and measures retrieval quality. Not designed to diagnose patients independently.
Records performance, identifies weak concepts, retrieves relevant material, adapts subsequent questions and gives teachers evidence of progress.
Extracts candidate evidence, compares it to defined requirements, flags missing information and produces explainable recommendations for human review. Discriminatory automated hiring logic is outside the programme.
Identifies obligations, dates, unusual clauses, defined terms and risks, linking each extracted point to its source text.
Reads statements, filings and approved sources, extracts relevant information, identifies changes and provides evidence-backed analysis with appropriate controls. Not marketed as autonomous financial advice.
Connected to structured product data, respects constraints, compares options, explains recommendations, handles inventory change and supports lead workflows.
Combines manuals, maintenance history and fault information to surface relevant evidence, guide troubleshooting and evaluate retrieval accuracy.
Prepares assets, records variations, supports controlled testing, analyses campaign data and produces traceable reports for human approval.
Retrieves approved answers, uses authorised tools, escalates when it cannot resolve an issue safely, and records unresolved cases.
Retrieves from distributed organisational documents, respects access rules, cites sources, measures answer quality and coordinates specialised agents across workflows.
Whether you are seeking a first AI engineering role or moving into the field from another technical job, preparation is integrated into the later part of the programme rather than treated as an afterthought.
Inside the curriculum, the interview and portfolio module (eight hours) covers a resume built from real technical work, repository presentation, system design for LLM applications, coding and architecture interview practice, debugging interviews, mock technical interviews and capstone presentation.
Beyond classroom work, students interact with practising engineers, receive introductions where relevant openings exist, and take part in an employer-facing demo day. Placement support is provided; employment depends on performance, hiring requirements and available opportunities, and cannot be guaranteed. The same preparation applies to internal moves and lateral transitions for working professionals.
When comparing programmes, the useful question is what a student can show an employer. Graduates of this programme are expected to present individual repositories, live applications, capstone architecture, technical design documents, assessment checkpoints, project demonstrations and mock-interview readiness, all of which can be opened or observed by someone outside Intraneurals.
Co-founder, Intraneurals
Works across intellectual property, technology commercialisation and regulatory practice, advising AI startups and working on AI patents in India and North America, with attention to how systems are built, claimed and protected.
Co-founder and Chief Executive, Intraneurals
Software delivery background including TCS and Capgemini, with experience across large engineering teams. Has trained engineers for several years and now works across applied AI engineering and teaching design.
Together they design the curriculum, the products the teaching draws on, and the methods the programme uses. Core modules are delivered by faculty and practitioners who currently work with AI and software systems. Industry mentors contribute through selected sessions, project reviews, capstone feedback and mock interviews.
Fully live and online (not recorded self-paced content). Weekly live session days and timings are confirmed for each cohort during the admission conversation, and live sessions are scheduled with working-professional students in mind.
The missed-session policy is confirmed at onboarding along with the session schedule, so it is specific to the cohort you join rather than generic. Ask about it directly in the admission conversation before you confirm your seat.
The Month 4 engineering assessment is the one built for this: students who need it enter structured remediation before continuing, rather than being dropped or carried forward silently. See assessment and published standard for all four checkpoints.
No. You select a capstone domain early, with guidance from the teaching team, and develop it as each engineering topic is introduced. See capstone domains for the range of problems past this level of system design.
No programme can honestly guarantee employment. Placement support (introductions, an employer-facing demo day, interview preparation) is provided, and outcomes depend on your performance, hiring requirements and available opportunities. See career support.
Join the waitlist via WhatsApp or and you will be contacted for the next cohort.
Batches are kept small so project supervision stays specific. Admission is straightforward: write to us, talk through fit and timing, and confirm your seat.
Next batch . Applications close .
If applications are closed or the cohort is full, join the waitlist via WhatsApp or .
If you want to understand whether this cohort fits your background and schedule, write to admissions. We would rather answer precise questions than ask you to infer everything from a brochure.
