Prompting an AI tool is not the same as engineering an AI system. Real capability comes from understanding data, models, evaluation, deployment and what happens after a model reaches users.
The NeoNex MindX AI/ML Engineering course takes you from Python and machine learning foundations to deep learning, NLP, computer vision, generative AI, RAG, agentic workflows, APIs and MLOps.
Share your coding level, education and target role. A NeoNex MindX advisor will help you understand whether you should begin with Data Science, Machine Learning or the full AI/ML Engineering program.
| Program Duration | 6–7 months |
|---|---|
| Training Mode | Live online, interactive sessions |
| Recommended Foundation | Basic programming comfort is helpful |
| Core Learning Areas | Machine learning, deep learning, NLP, computer vision, LLMs, RAG, APIs and MLOps |
| Learning Access | LMS, recordings, notes and resources |
| Practical Learning | Assignments, projects and portfolio outputs |
| Internship | 3–6 month component with certification |
| Career Support | Resume, profile, interview and placement assistance |
| Batch Options | Weekday morning, weekday evening and weekend |
An AI/ML Engineering course teaches you how to build, deploy and maintain software systems that use machine learning and artificial intelligence.
It combines data preparation, model development, deep learning, AI application development, APIs, cloud foundations and model-lifecycle practices.
This online AI and Machine Learning course in India covers Python, statistics, machine learning, TensorFlow or PyTorch, computer vision, natural language processing, generative AI, large language models, embeddings, vector databases, retrieval-augmented generation, agentic workflows, FastAPI, MLOps and deployment foundations.
The program is designed to take you beyond demonstrations and toward production-oriented thinking.
Many AI courses stop after training a model or showing a chatbot demonstration. Employers and project teams need people who understand the complete system around the model.
Build applied AI skills beyond academic algorithms and isolated assignments.
Progress from model building into deep learning, LLM applications, deployment and MLOps.
Learn to integrate AI models and services into real applications and APIs.
Move toward machine learning and AI product roles through a structured technical bridge.
Add practical generative AI, automation and model-lifecycle capability to existing experience.
Enter through the Python and machine learning foundation modules and prepare for a technically demanding path.
Prepare data, write modular code and understand the inputs that models depend on.
Study machine learning, metrics, optimisation and error analysis before moving into advanced AI.
Build deep learning, computer vision and NLP applications.
Work with LLM concepts, embeddings, vector retrieval, RAG and controlled agentic workflows.
Create an API or application and understand versioning, monitoring, drift and release practices.
Consider access, scalability, cost, privacy, evaluation and quality controls.
Explain the architecture, evaluation, limitations, deployment and maintenance plan.
The curriculum progresses from coding and mathematical foundations to machine learning, deep learning, specialised AI applications, generative AI, deployment, MLOps and a production-oriented capstone.
Build Python, data preparation, mathematics, statistics and core machine learning capability.
Modules: Python & Data Preparation · Mathematics & Statistics · Machine Learning · Advanced ML
Develop neural-network, computer-vision and natural-language-processing applications.
Modules: Deep Learning · Computer Vision with CNNs · NLP and Sequence Models
Build LLM, embeddings, vector-search, RAG, agentic automation and application-serving workflows.
Modules: Generative AI, LLMs & Agentic AI · AI API Development & Deployment
Learn model lifecycle management, cloud foundations and production-oriented project delivery.
Modules: MLOps · Cloud AI Foundations · AI Engineering Capstone
What you will build: An AI-ready preprocessing pipeline.
What you will build: A mathematics-for-models notebook.
What you will build: A multi-algorithm machine learning project.
What you will build: A tuned and documented production-candidate model.
What you will build: A neural-network classification project.
What you will build: An image-classification or recognition system.
What you will build: A sentiment-analysis or named-entity-recognition application.
What you will build: A RAG assistant, content system or agentic automation prototype.
What you will build: A deployed AI API or web application.
What you will build: A versioned model pipeline with a monitoring plan.
What you will build: A cloud deployment architecture diagram.
What you will build: A production-oriented AI portfolio project.
Each tool is taught within a defined AI workflow rather than as an isolated software feature.
Each project should include the use case, data or knowledge source, evaluation approach, application layer, documentation and limitations.
Train and evaluate a model, expose inference through an API and document inputs, outputs and failure cases.
Prepare images, train or adapt a CNN and create an interface for prediction and review.
Process text, evaluate model behaviour and deliver a useful output for a business or service workflow.
Use embeddings, vector search and retrieved context to answer questions from a controlled knowledge source.
Connect tools or steps in a controlled automation and define boundaries, checks and human-review points.
Combine model development, an API or application, deployment architecture, versioning and a monitoring plan.
Connect data preparation, machine learning, deep learning, generative AI, APIs, deployment and monitoring.
Understand why a method works and how to evaluate it before relying on a library or model API.
Build systems that include model logic, application access, documentation and lifecycle thinking.
Debug code, review architecture and receive mentor feedback while building projects.
Discuss hallucination, bias, privacy, evaluation, model limits and quality controls as part of implementation.
Strengthen GitHub, project explanation, resume positioning and technical-interview readiness.
Learn machine learning, AI engineering, projects and professional readiness with trainers covering data, AI, development, QA, DevOps and communication skills. Trainer allocation may vary by module and batch.
Trainer feedback focuses on model behaviour, application architecture, deployment thinking and the ability to explain technical decisions clearly.
Developer & QA
LinkedIn Profile →DevOps
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Data Analytics, Business Analytics, Data Science & AI
LinkedIn Profile →
Data Analytics, Business Analytics, Data Science & AI
LinkedIn Profile →Data Analytics, Business Analytics, Data Science & AI
LinkedIn Profile →Data Analytics & Data Engineering
LinkedIn Profile →Developer & QA
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Communication Skills, Soft Skills & Profile Building
LinkedIn Profile →The program includes a 3–6 month internship component with certification, subject to current program requirements and completion of the relevant learning and project milestones.
Important: AI roles often require strong programming, projects and technical-interview performance. Role eligibility varies by experience and employer. NeoNex MindX provides placement assistance, not guaranteed employment.
Understand Career SupportThe curriculum supports preparation for technically demanding roles. Job titles and eligibility vary by employer, prior experience, programming depth and portfolio quality.
| Comparison Point | AI/ML Engineering | Data Science |
|---|---|---|
| Primary Focus | Building and operating AI-enabled systems | Analysing data and building predictive models |
| Core Depth | Deep learning, NLP, computer vision, LLMs, APIs and MLOps | Statistics, experimentation, feature engineering and model analysis |
| Typical Output | AI application, API, RAG system or monitored model pipeline | Model, experiment, notebook, dashboard or prediction application |
| Software Engineering Emphasis | Higher | Moderate |
| Production Lifecycle Emphasis | Deployment, monitoring, versioning and maintenance | Model development, analysis and evaluation |
| Choose This When | You want to develop and deploy AI products or services | Your main interest is data analysis and predictive modelling |
Monday to Friday
7:00 AM–9:30 AMMonday to Friday
8:00 PM–10:30 PMSaturday and Sunday
9:00 AM–1:30 PMBatch availability and start dates may vary. Confirm the current schedule during counselling.
Check the Next Available BatchAI/ML Engineering is the practice of building software systems that use machine learning or artificial intelligence. It includes data preparation, model development, application integration, deployment, monitoring, versioning and continuous improvement.
No. Generative AI is one part of the program. The curriculum begins with Python, mathematics and machine learning, then covers deep learning, NLP, computer vision, LLMs, RAG, agentic AI, APIs, MLOps and cloud foundations.
Basic coding experience is helpful because AI engineering is a technical field. Beginners can start with the Python foundation module, but should be ready to practise coding regularly and complete technical assignments.
The curriculum includes scikit-learn for machine learning and TensorFlow or PyTorch for deep learning foundations. Specific libraries may be updated as tools evolve while preserving the core learning outcomes.
Yes. The program covers applied LLM concepts, prompting, embeddings, vector databases, semantic search, retrieval-augmented generation, evaluation, quality controls and a RAG-based project.
Yes. Learners cover text preprocessing, sentiment analysis, named-entity recognition, sequence-model concepts, image preparation, CNNs, classification, transfer learning and object-detection awareness.
MLOps applies engineering practices to the machine learning lifecycle. It covers versioning, reproducibility, release workflows, monitoring, drift awareness, retraining and documentation so models can be maintained after deployment.
Project themes can include a prediction API, computer-vision system, NLP application, RAG assistant, agentic automation and an end-to-end AI capstone with deployment and monitoring foundations.
Yes, especially for learners with a computer science, engineering, mathematics or data background. Freshers from other fields can also join, but may need additional time for Python, mathematics and software foundations.
The program includes a 3–6 month internship component with certification, subject to current program requirements and completion of the relevant learning and project milestones.
Support can include resume and LinkedIn development, GitHub and project review, mock technical interviews, system-design discussion, portfolio presentation and relevant opening or referral support where applicable. A job is not guaranteed.
Call 084318 03839 or complete the enquiry form. A NeoNex MindX advisor will share the current fee, available EMI options, upcoming batch, schedule and detailed syllabus.
Get the full AI/ML Engineering syllabus and speak with a course advisor to assess your coding foundation, course fit and next batch.