Foundations that hold
Python, linear algebra, calculus and statistics — enough to reason about a model instead of calling a library and hoping.
Admissions open 24 weeks · Live Online · small cohorts
Become an Artificial Intelligence Engineer
Most AI courses stop at the notebook. This one runs machine learning, deep learning and generative AI alongside the engineering that gets a model deployed, measured and kept alive in production.
- Python to production: ML, deep learning, NLP, computer vision and generative AI with LLMs.
- Build and deploy 6 real systems — served, evaluated and monitored, not left in a notebook.
- Finish with a defended capstone and a portfolio you can walk an interviewer through.
No obligation. We’ll send the current fee structure, cohort dates and a full syllabus.
24 weeksLive, part-time
12 modulesAcross both tracks
6 buildsDeployed, not simulated
30 seatsPer cohort
Get the syllabus & fees
Tell us where to send it. An advisor follows up within one working day.
Program overview
A single program for the two halves of the job
An AI engineer has to do two things well, and most courses only teach one. You have to be able to build the model — the maths, the training, the evaluation. And you have to be able to ship it — the pipeline, the API, the monitoring, the retraining. We teach both, in the same twenty-four weeks, running in parallel rather than one after the other.
Build the model
Machine learning, deep learning with PyTorch, NLP and computer vision — trained on real datasets with honest evaluation.
Work with foundation models
Generative AI, large language models, fine-tuning, retrieval-augmented generation and agents — treated as engineering, not prompt folklore.
Ship it and keep it alive
Serving, Docker, CI/CD, drift detection and cost control. A model that only runs on your laptop has not been built yet.
Why AI, why now
The bottleneck moved from models to deployment
Capability is no longer the constraint. Anyone can reach a frontier model with an API key, and a pretrained network is a download away. What companies cannot do is get those models working against their own data, their own permissions and their own half-documented systems — and keep them working once the data shifts.
That is why hiring has moved from research profiles towards engineers who can take a problem all the way through: frame it, model it, evaluate it honestly, deploy it, and watch it. Someone who can train the model and operate it.
Last mile Where AI projects stall — data quality, evaluation, integration and monitoring, not model choice.
Full stack Modelling depth plus production engineering. Rare in one person, and priced accordingly.
Beyond LLMs Generative AI taught in depth, on top of the classical ML that still runs most production systems.
Durable Frameworks change every quarter. Statistics, evaluation and deployment discipline don’t.
Curriculum
12 modules across two parallel tracks
The AI & ML track carries the modelling spine — maths, machine learning, deep learning, NLP, computer vision and generative AI. The production track runs alongside it from week four, so you are training models and deploying them in the same week. Expand any module to see what it covers.
01 Python engineering and the scientific stack
The programming floor the rest of the course stands on. Written as code you could put in front of a reviewer, not throwaway notebook cells.
- Python for data work: typing, packaging and project layout
- NumPy arrays, broadcasting and vectorised thinking
- pandas for real, messy datasets
- Visualisation with Matplotlib and Seaborn
- Jupyter for exploration, modules for anything that survives
- Git, testing and CI basics
02 Mathematics and statistics for machine learning
Enough linear algebra, calculus and statistics to reason about a model instead of calling library functions and hoping.
- Vectors, matrices and the algebra behind a forward pass
- Derivatives and gradient descent from first principles
- Probability, distributions and Bayes in practice
- Descriptive statistics, sampling and confidence
- Hypothesis testing and A/B analysis
- Why a metric can be right and still mislead you
03 Data engineering, SQL and feature pipelines
Models are mostly a data problem. This is the part that decides whether anything downstream works.
- SQL you can reason about, and PostgreSQL in anger
- Ingestion, cleaning and handling missing data honestly
- Feature engineering, encoding and scaling
- Train/validation/test splits and leakage you did not notice
- Batch pipelines with Airflow, transformations with dbt
- Versioning data so an experiment can be reproduced
04 Core machine learning
Supervised and unsupervised learning end to end, with the evaluation discipline that separates a good score from a good model.
- Regression, classification and the bias–variance trade-off
- Decision trees, random forests and gradient boosting
- XGBoost and LightGBM on tabular problems
- Clustering, dimensionality reduction and PCA
- Cross-validation, hyperparameter search and Optuna
- Imbalanced data, and metrics that survive it
05 Deep learning and neural networks
Neural networks built up from the mechanics, then trained properly with PyTorch.
- Perceptrons, backpropagation and activation functions
- Training loops, optimisers, schedulers and regularisation
- PyTorch as the working framework, TensorFlow/Keras for context
- CNNs for images, RNNs and attention for sequences
- Transfer learning and fine-tuning pretrained models
- Reading a loss curve and diagnosing what is actually wrong
06 Natural language processing
From text preprocessing to transformer models, with the tasks that show up in real products.
- Tokenisation, embeddings and vector semantics
- Classification, named entity recognition and sentiment
- The transformer architecture, attention and why it won
- Hugging Face Transformers and the model hub
- Fine-tuning a language model on a domain dataset
- Evaluating language output when there is no single right answer
07 Computer vision
Teaching a system to read images, and knowing what it will get wrong.
- Image data, augmentation and building a usable dataset
- Convolutional architectures: ResNet, EfficientNet and friends
- Object detection and segmentation with YOLO
- OpenCV for the preprocessing nobody puts in the paper
- Vision transformers and multimodal models
- Deploying a vision model under a latency budget
08 Generative AI and large language models
Working with foundation models as components that are powerful, expensive and unreliable — all three at once.
- How LLMs are trained, aligned and served
- Prompt engineering as an engineering practice, not a craft secret
- Structured output, JSON schemas and validation
- Function and tool calling
- Fine-tuning, LoRA and when it beats prompting
- Context windows, token cost and latency budgets
- Diffusion models and image generation
09 RAG, AI agents and the Model Context Protocol
Most retrieval demos work and most retrieval systems don't. This module is the gap, plus the agent patterns that earn their complexity.
- Chunking and ingestion over documents nobody curated
- Embeddings, vector databases and hybrid search
- Re-ranking, query rewriting and metadata filters
- Grounding, citations and refusing to answer
- Agent loops, planning, tools and hand-offs
- MCP for connecting models to real systems
- Memory, state and human-in-the-loop checkpoints
10 Model evaluation, safety and responsible AI
The discipline that separates a demo from something a business will depend on, and the obligations that come with shipping a model at all.
- Building evaluation sets from real data without leaking it
- Offline metrics, online metrics and the gap between them
- LLM-as-judge, and when not to trust it
- Regression testing prompts and pipelines
- Bias, fairness auditing and explainability with SHAP
- Prompt injection, data leakage and access control
- Red-teaming your own system before anyone else does
11 MLOps: deployment, serving and monitoring
Getting a model out of your machine and keeping it healthy — the part most AI courses leave as an exercise.
- Serving models behind FastAPI, batching and latency
- Docker, environments and reproducible builds
- Experiment tracking and model registries with MLflow
- CI/CD for models with GitHub Actions
- Deploying on AWS or Azure, and what it costs
- Drift detection, retraining triggers and alerting
- Observability, tracing and cost dashboards
12 AI capstone and demo day
One end-to-end system against a real brief: scoped, built, evaluated, deployed and defended under questioning.
- Choosing a problem where AI is genuinely the right tool
- Data sourcing, modelling and iteration
- Evaluation report and honest known limitations
- Deployment with monitoring in place
- Live demo and technical defence
- Portfolio write-up and interview narrative
AI tools & technology stack
The stack you’ll actually deploy with
Tooling is taught as a means, not a syllabus. You learn the idea first — gradient descent, attention, retrieval, evaluation — then the framework that currently implements it best, so the skill survives the next release cycle.
Python & data foundations
Python NumPy pandas Matplotlib / Seaborn Jupyter PostgreSQL SQL foundations
Machine learning
scikit-learn XGBoost LightGBM statsmodels Optuna Hyperparameter search
Deep learning & neural networks
PyTorch TensorFlow / Keras Hugging Face Transformers OpenCV YOLO Detection & segmentation
Generative AI & LLMs
OpenAI GPT Anthropic Claude Google Gemini Llama / open weights LangChain LangGraph MCP Model Context Protocol
Vector search & RAG
pgvector FAISS Pinecone Qdrant Elasticsearch Hybrid search
MLOps & deployment
Docker MLflow FastAPI Airflow AWS Azure GitHub Actions Kubernetes
Evaluation & monitoring
Weights & Biases Evidently Drift detection Ragas RAG evaluation LangFuse SHAP Explainability Grafana
Hands-on projects
Every project starts with a business problem
Each build starts the way real work does: a business with a problem, not a tidy dataset with a target column. You frame it, model it, evaluate it and deploy it.
Customer churn prediction, served as an API
A subscription business loses customers it could have kept, and finds out a month too late.
Build the full path from raw customer data to a deployed prediction endpoint — feature engineering, model selection, threshold tuning against the actual cost of a false alarm.
pandas scikit-learn XGBoost FastAPI Docker
Visual quality inspection on a production line
A manufacturer inspects components by eye and misses defects on the night shift.
Train and deploy an image classifier that flags defective parts, including the augmentation and threshold work that makes it usable when lighting changes.
PyTorch OpenCV transfer learning ONNX
Résumé screening with semantic search
A recruitment team reads 2,000 applications a month and keyword filters keep rejecting good candidates.
Parse unstructured documents, embed them, and rank against a role description — with an evaluation set that proves it beats the keyword filter it replaces.
Hugging Face Transformers embeddings pgvector
Demand forecasting with drift monitoring
A retailer's forecast was accurate at launch and quietly decayed over two quarters.
A forecasting pipeline that retrains on a schedule, watches its own inputs for drift, and raises an alert before the business notices the error.
Time series models Airflow MLflow Evidently
Knowledge assistant with citations and access control
A policy team wants a question-answering assistant, but half the source documents are restricted.
Retrieval that respects per-user permissions and cites its sources, so the assistant answers what the asker is allowed to know — and declines rather than inventing.
RAG vector search re-ranking row-level security
End-to-end AI capstone
A problem you scope yourself, from dataset to deployed and monitored system.
Problem framing, data work, modelling, evaluation, deployment and a written account of what the system cannot do — presented and defended on demo day.
Your choice justified in the architecture document
Real-time issue & solution
What the job looks like when it's real
A worked AI build, milestone by milestone: the brief that was wrong, what the data actually showed, what shipped first, what had to be retrained, and what it earned.
−94% Historical defect lookup
−97% Warranty claim → batch trace
Consistent Inspection recall, 10 of 11 defect families
Learning methodology
Built the way the work actually runs
No week is passive. The pattern repeats: frame the problem, build something that works, have it torn apart, then fix it. It is uncomfortable and it is the fastest way we know to make the job feel familiar before you're doing it for real.
Cohorts are capped so every submission gets read by a human, and every learner presents at least twice before the capstone.
Live build sessions
Two to three sessions a week, taught live with code on screen. Recorded, but designed to be attended.
Weekly shipped increment
Every week ends with something running. Reviewed against the standards a production team would apply.
Model post-mortems
You present a result and defend the evaluation behind it, with a mentor pushing on leakage, baselines and what the metric hides.
Code & architecture review
Small-group review with a practising engineer. Where most of the real learning happens.
Capstone & defence
A full build you present and defend under questioning, exactly as you would in a technical interview.
Career opportunities
Roles this program prepares you for
The same skill set opens several adjacent titles. Salary figures are market ranges compiled from public sources for 2026 — context for planning, not a promise from us.
| Role | What you’d own | India (indicative) | Global (indicative) |
|---|---|---|---|
| AI Engineer | Builds the application layer over models — data path, inference, retrieval, evaluation and the serving stack behind it. | ₹12–35 LPA | $120k–$220k |
| Machine Learning Engineer | Owns models in production: training pipelines, feature stores, serving, retraining and the metrics that decide when to intervene. | ₹14–38 LPA | $130k–$240k |
| Generative AI / LLM Engineer | Specialises in foundation models — prompting, fine-tuning, retrieval, agents, evaluation and keeping token cost survivable. | ₹15–40 LPA | $140k–$260k |
| Data Scientist | Frames business problems as measurable questions, builds the models that answer them, and communicates the result to people who will act on it. | ₹10–30 LPA | $110k–$190k |
| MLOps Engineer | Builds the platform other teams ship models on: pipelines, registries, deployment, monitoring, drift detection and cost control. | ₹14–35 LPA | $125k–$210k |
| Computer Vision / NLP Engineer | Deep specialisation in one modality — imaging, document understanding, speech or language — and the deployment constraints it brings. | ₹12–32 LPA | $120k–$200k |
Ranges vary widely with prior experience, company and location. We share what we see in the market; we don’t guarantee a package, and we’d be wary of anyone who does.
Who should join
This is a good fit if you already write code
The program moves quickly and assumes you can build. What it adds is the mathematics, the modelling depth and the production engineering around them.
- Software engineers moving into AI product work
- Data analysts and data engineers stepping up into modelling
- IT professionals changing track who can already program
- Final-year and recent CS graduates with genuinely solid fundamentals
Prerequisites
What we assume you bring
We assess these in the admission call rather than taking them on trust — it is fairer to everyone than discovering the gap in week three.
- Comfortable programming in at least one language
- Working knowledge of Python — you can build and debug a small application
- Basic SQL and an understanding of relational data
- Familiarity with APIs, HTTP and Git
- School-level mathematics you are willing to rebuild — we start from the basics
- A laptop with 16 GB RAM. No GPU needed — training runs on hosted compute
Prior machine-learning theory is not required. The maths, statistics and AI content are built from the ground up.
Duration, batches & mode
Pick the cohort that fits your week
Twenty weeks either way. Around 6 hours of live sessions plus 6–10 hours of build work per week — the capstone weeks are heavier.
Weekend Cohort
Enrolling- Duration
- 24 weeks
- Mode
- Live online
- Schedule
- Sat & Sun, 10:00 AM – 1:00 PM IST
- Seats
- 30 per cohort
Weekday Evening Cohort
Enrolling- Duration
- 24 weeks
- Mode
- Live online
- Schedule
- Mon, Wed & Fri, 8:00 – 10:00 PM IST
- Seats
- 30 per cohort
Mentorship
Small cohorts, practising mentors
Every learner is assigned a mentor for the whole program — the same person across all twenty-four weeks, so the feedback compounds instead of resetting.
- Weekly small-group code and architecture review
- One-to-one checkpoints at weeks 6, 12 and 20
- Model and architecture critiques with no scores softened
- Cohort channel that stays open after you finish
Certification
Certified on what you built
On completion you receive the AI Engineer certificate. It is awarded on the capstone, not an exam — you certify by building, deploying and defending a working system.
Program certificate
Issued on successful capstone defence, with a verifiable reference.
Documented portfolio
Your deployed builds, written up with modelling decisions, evaluation and known limitations — what actually moves an AI interview.
Placement & career support
What we do, and what we don’t promise
We will put real work into getting you interview-ready and in front of people. We won’t promise you a job or a number — no training institute controls either, and pretending otherwise is how learners get misled.
Portfolio positioning
Your projects written up as case studies — problem, decisions, trade-offs, outcome — and a résumé rebuilt around them.
Interview preparation
Mock rounds for the full AI loop: machine learning fundamentals, coding, system design and behavioural.
Job-search strategy
Which roles to target with your background, how to read a job description honestly, and where we can make an introduction we actually have.
Alumni network
Continued access to the cohort channel and alumni community after you finish.
Profile review
LinkedIn and GitHub reviewed by someone who has hired for these roles, not a template.
Refresher access
Re-attend any module in a later cohort at no cost within twelve months.
Instructors & mentors
Taught by people who do the job
Every session is led by someone currently building and deploying AI systems — not a full-time trainer working from slides.
Instructor profiles for the upcoming cohort are being finalised. Ask an advisor and we’ll share the panel for your batch.
Questions
Frequently asked questions
Straight answers. If yours isn’t here, call or message us — we’d rather talk than guess.
What is this artificial intelligence course, and what will I be able to do at the end?
It is a 24-week Artificial Intelligence Engineer program that takes you from Python through machine learning, deep learning, NLP, computer vision and generative AI, and out the other side into deployment and monitoring. The measure of success is not a score in a notebook: by the end you have built and deployed working AI systems, evaluated them honestly, and documented what they cannot do. You leave with a portfolio, not a certificate alone.
How is this different from a data science or machine learning course?
Most courses stop where the model stops — you train something, report an accuracy figure, and never deploy it. Here the production track runs in parallel from week four: data pipelines, evaluation discipline, serving, drift monitoring and MLOps. Roughly two-thirds of the program is modelling and two-thirds overlaps with engineering, because the two are taught in the same weeks rather than in sequence.
Does the course cover generative AI and large language models?
Yes, and substantially — four weeks on generative AI and LLMs, then a dedicated module on retrieval-augmented generation, AI agents and the Model Context Protocol. That covers prompt engineering, structured output, function calling, fine-tuning and LoRA, vector search, grounding and citations, and the evaluation work that keeps an LLM feature honest. Classical machine learning is still taught first, because most production AI problems are still not language problems.
Which AI tools, frameworks and technologies will I learn?
Python with NumPy, pandas and scikit-learn; XGBoost and LightGBM for tabular work; PyTorch as the main deep learning framework with TensorFlow and Keras for context; Hugging Face Transformers, OpenCV and YOLO; the major model APIs including OpenAI, Anthropic Claude and Google Gemini; LangChain, LangGraph and MCP; vector stores including pgvector, FAISS, Pinecone and Qdrant; and MLflow, FastAPI, Docker, Airflow and GitHub Actions for deployment. Tools are taught as implementations of a pattern, so the skill outlives the release cycle.
Who should join this AI course?
Software engineers moving into AI, data analysts and data engineers stepping up into modelling, IT professionals changing track, and final-year or recent computer science graduates with genuine programming fundamentals. It also suits working developers who have used an LLM API and hit the ceiling of what prompting alone can do.
What are the prerequisites for the artificial intelligence program?
Comfortable programming in at least one language, some working Python, and school-level mathematics you are willing to rebuild. Basic SQL helps. Prior machine learning experience is not required — the maths and statistics module assumes nothing and builds from the ground up. What we cannot teach quickly is the ability to write and debug real code, so that is the bar for admission.
Can a fresher or a non-programmer join?
Freshers with solid programming fundamentals do well, and we assess that in the admission call rather than assuming it. If you have never programmed, this is the wrong starting point — the first module moves fast and assumes you can already write and debug a small application. We will tell you honestly on the call if the fit is not there.
How much mathematics do I really need?
More than a bootcamp implies and less than a degree demands. Weeks two to four rebuild the linear algebra, calculus and statistics that machine learning actually uses, aimed at reasoning about a model rather than proving theorems. If you can follow an equation with effort, you will be fine.
How long is the course and what is the weekly time commitment?
Twenty-four weeks, delivered live. Expect around 6 hours of live sessions and 8 to 12 hours of independent build work each week. The deep learning and capstone stretches are heavier. Sessions are recorded, so a missed class is recoverable — the build work is not optional, and it is where the learning actually happens.
Is the AI training online or in a classroom?
Live online, in small cohorts, with instructors present rather than pre-recorded video. Sessions are recorded for your cohort, mentors hold weekly doubt-clearing slots, and project reviews are one-to-one. Demo day is run as a live online event.
Do I need a powerful laptop or a GPU?
A laptop with 16 GB of RAM is enough. Deep learning training runs on hosted GPU notebooks and the generative AI modules use hosted model APIs, so no local GPU is required. Cloud credits for the training and deployment modules are covered within the program.
What happens if I miss a live session?
Every session is recorded and available to your cohort. Mentors hold weekly doubt-clearing slots, so you can catch up on the concept and still submit that week's build on time.
What jobs can I apply for after completing the AI course?
AI Engineer, Machine Learning Engineer, Generative AI or LLM Engineer, Data Scientist, MLOps Engineer, and specialist Computer Vision or NLP Engineer roles. These sit across AI product companies, enterprise platform teams, consultancies, global capability centres and the analytics functions of banks, healthcare and retail.
Do you provide placement support or a job guarantee?
We provide career support: portfolio review, a résumé and positioning rewrite built around your deployed projects, and mock technical and behavioural interviews. We do not promise a job, a specific salary or a specific interview outcome — no training provider controls those, and any that guarantees them is selling you something it cannot deliver.
What salary can an AI engineer expect?
Market ranges as of 2026 sit roughly between ₹12 and ₹40 LPA in India and $120,000 to $260,000 globally, depending on the role, with generative AI and machine learning engineering at the upper end. Compensation depends heavily on your prior experience, the company and the location. Treat these as market context compiled from public sources, not as a figure we can promise you.
What is the course fee, and are instalments available?
Fees vary by cohort and are shared on the admission call along with current instalment options and any early-enrolment concession. Submit the enquiry form and an advisor will send you the current fee structure and cohort dates.
Will I get a certificate?
Yes. On completion you receive an AI Engineer certificate, awarded on the capstone rather than a written exam — you certify by delivering, deploying and defending a working system. The documented portfolio you leave with carries far more weight in an interview than the certificate does.
Talk to us
Tell us where you are, and we’ll tell you honestly whether this fits
A twenty-minute call with an advisor. You’ll get the full syllabus, the current fee structure and cohort dates — and a straight answer on whether your background is ready for this program or would be better served by something else first.
Request a callback
Fields marked * are required. Everything else just helps us prepare.
The engineers who can ship AI are the ones who get to build it
Twenty-four weeks, a capped cohort and a portfolio of AI systems you actually deployed. Start with a conversation — we’ll tell you straight whether it’s the right move.