SAARATHI
Featured trackSoftware EngineeringCareer Track
AI Engineering & MLOps Course in Nepal — LLMs, Generative AI, Model Deployment & Production ML
Python + ML basics to production AI in 3 months - foundations-first ramp into LLMs, RAG, agents, fine-tuning and MLOps with a deployed capstone.
PythonNumPypandasscikit-learnPyTorchHugging Face TransformersOpenAI APIAnthropic API
Tuition & Support
Initial batch offer
NPR 40,000Flexible options available
NPR 34,000
Save NPR 6,000 on this course.
Duration12 weeks (3 Months)
Schedule5 days a week
Daily2 hrs a day
OutcomeJunior AI Engineer / Applied ML Engineer
Upcoming Batch
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Limited seats. Final class timing is confirmed within 48 hours based on availability.
Upcoming Batches
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Why This Course?
Why This Course?
12 weeks · 120 guided hours · genuine basics-first ramp for anyone comfortable with Python.
You move through 4 practical phases over 12 weeks: Python, ML & Deep Learning Foundations, Agent Workflows & Advanced Retrieval, and Model Adaptation, Local Inference & Evaluation.
Target Audience
Who Is This Course For?
- Python-comfortable beginners who want a real AI engineering career (not just ChatGPT wrapping)
- Data scientists transitioning from notebook analysis into engineering and deployment
- Full stack or backend developers specialising into AI systems
- ML learners who want production discipline around what they already know
- Builders who want to ship real LLM + RAG + agent products instead of demos
Skills You'll Master
PythonNumPypandasscikit-learnPyTorchHugging Face TransformersOpenAI APIAnthropic APILangChainLlamaIndexPineconeChromaDBpgvectorLangGraphLangSmithRAGASLoRAQLoRAOllamallama.cppFastAPIDockerMLflowGitGitHub
Curriculum
Curriculum
A structured journey to mastery
W1
Topics
Python dev environment, Jupyter, VS Code, Git, Refresher - functions, classes, async, type hints, numpy and pandas for AI workflows, Data loading and cleaning patterns, Working with HuggingFace datasets, AI assistants (Copilot, ChatGPT, Cursor) from day one, First scikit-learn model on a real dataset
W2
Topics
Supervised vs unsupervised learning, Train/val/test discipline and overfitting, Linear and logistic regression, Decision trees and random forests, Classification metrics (precision, recall, F1, ROC-AUC), Regression metrics (RMSE, MAE, R²), Feature engineering basics, Why classical ML still matters before LLMs
W3
Topics
Neural networks from scratch - the math that matters, PyTorch tensors, autograd, training loop, CNNs and RNNs at awareness level, Attention and transformer architecture intuition, Tokenisation and embeddings, HuggingFace Transformers library tour, First text classification model fine-tuned on a small dataset
| Week | Topics |
|---|---|
| W1 | Python dev environment, Jupyter, VS Code, Git, Refresher - functions, classes, async, type hints, numpy and pandas for AI workflows, Data loading and cleaning patterns, Working with HuggingFace datasets, AI assistants (Copilot, ChatGPT, Cursor) from day one, First scikit-learn model on a real dataset |
| W2 | Supervised vs unsupervised learning, Train/val/test discipline and overfitting, Linear and logistic regression, Decision trees and random forests, Classification metrics (precision, recall, F1, ROC-AUC), Regression metrics (RMSE, MAE, R²), Feature engineering basics, Why classical ML still matters before LLMs |
| W3 | Neural networks from scratch - the math that matters, PyTorch tensors, autograd, training loop, CNNs and RNNs at awareness level, Attention and transformer architecture intuition, Tokenisation and embeddings, HuggingFace Transformers library tour, First text classification model fine-tuned on a small dataset |
Your Learning Schedule
Mon-Fri live classes:
2 hrs live class/day + 2 hrs self-study at home (required).
Sunday Open Classroom:
Classrooms and labs stay fully open all day. Come study, pair-program, and build.
Daily commitment:
Minimum 2 hrs focused practice beyond class at home. This is what builds real mastery.
Career Outcomes
Where this course can lead
These are examples of roles, responsibilities, or directions this course can help you grow toward.
Possible next roles
Junior AI Engineer / Applied ML Engineer
AI Engineer / MLOps Engineer
Senior AI Engineer / ML Platform Engineer
AI Solutions Architect / Applied AI Lead
FAQ
Questions we
actually get asked.
Can't find yours? Send an inquiry or visit Old Baneshwor. We'll give you a straight answer before you commit.
The course fee is NPR 40,000 for the full 12-week program. This includes all training, mentor sessions, lab access, and career support. Contact us to discuss installment options.
How Saarathi Gate shapes your learning plan
Saarathi Gate is a diagnostic, not a pass-or-fail exam. It helps us understand your current skill level, how you learn best, where you are already strong, and where you need extra support before the batch begins.
Before the batch starts
You complete the Gate Assessment — an aptitude test that maps your strengths, weaknesses, learning style, and pace — before the batch begins. Your personal plan is built from that.
During the course
Trainers use that diagnostic profile to guide pacing, practice focus, feedback, and the kind of support that helps you learn best.
Certification and proof of work
Certification for AI Engineering & MLOps Course in Nepal — LLMs, Generative AI, Model Deployment & Production ML depends on attendance, required coursework, trainer review, and the practical work described in the micro-syllabus and full syllabus.
Seats are limited
Ready to start your journey?
Reserve your seat, complete Saarathi Gate, and start the batch with clearer guidance on your level, pacing, and practical focus.
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