AI Engineering & MLOps Course in Nepal — LLMs, Generative AI, Model Deployment & Production ML
Build production AI systems with RAG, agent workflows, evaluation, and MLOps discipline.
Your Learning Schedule
2 hrs live class/day + 2 hrs self-study at home (required).
Classrooms and labs stay fully open all day. Come study, pair-program, and build.
Minimum 2 hrs focused practice beyond class at home. This is what builds real mastery.
Production AI thinking: structured outputs, retries, guardrails, and cleaner service boundaries
RAG systems with stronger retrieval, chunking, evaluation, and citation habits
Agent workflows with LangGraph and multi-step orchestration patterns
Mar 1, 2026 – Dec 31, 2099
Save NPR 6,000 on this course right now.
Live interactive sessions with trainers
Small-batch feedback on your practical work
Course materials, recordings, and lab support
Personalized guidance after Saarathi Gate on pacing, practice focus, and support
Upcoming Batch
Enrolling Now
Limited seats. Final class timing is confirmed within 48 hours based on availability.
About this Course
This course is built for learners who already have data and ML foundations and now need production AI engineering discipline. The focus is not only on using LLM APIs, but on structuring outputs, improving retrieval quality, evaluating responses, and building systems other people can review and maintain.
Students move from notebook-level experimentation into more dependable AI workflows: RAG systems, tool-using agents, evaluation pipelines, local inference awareness, and MLOps habits that make deployment less fragile.
Production AI thinking: structured outputs, retries, guardrails, and cleaner service boundaries
RAG systems with stronger retrieval, chunking, evaluation, and citation habits
Agent workflows with LangGraph and multi-step orchestration patterns
Fine-tuning and local inference taught with judgment instead of hype
MLOps delivery with MLflow, FastAPI, monitoring, and trainer-reviewed capstone work
Capstone spotlight
AI-Powered Document Intelligence Platform
> Note: Project themes may evolve each batch, but every learner still completes one trainer-approved final project from the approved pool. Smaller guided exercises can happen during the course, but the public completion standard stays anchored to one polished final outcome.
Likely stack
- Multi-format document ingestion pipeline (PDF, DOCX, web)
- Advanced RAG with re-ranking and citation tracking
- AI agent for complex multi-step document analysis
- Fine-tuned model for domain-specific extraction and evaluation
Why This Course?
Global Context: Teams are moving from AI demos to production systems that need clearer retrieval, evaluation, guardrails, and monitoring. The engineering challenge is no longer just calling a model. It is making the system dependable.
Nepal Context: Nepal's AI ecosystem continues to grow through product companies, service teams, research organizations, and remote engineering work. The gap is not only model access. It is in engineers who can turn AI ideas into maintainable services with testing, monitoring, and better cost awareness.
Your Opportunity: This course is built for the learner who already has ML basics and now wants stronger production judgment. You will practice RAG, agents, evaluation, local inference awareness, and MLOps in a way that rewards reliability over hype.
The strongest AI engineers are usually the ones who can explain why a system retrieved the wrong context, why a prompt failed, why a model should not be fine-tuned yet, and how to monitor quality after launch.
Reliable retrieval, evaluation, and monitoring habits matter more than demo polish
Teams are moving from AI experiments to production services
Structured outputs, retries, and system boundaries become core engineering work
Prompting alone stops being enough in production
Knowing when not to fine-tune is part of good judgment
Fine-tuning is accessible but often overused
Versioning, evaluation, and rollback discipline create real team value
MLOps gaps slow down AI delivery
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 Saarathi Gate so we can understand your current skill level, how you learn best, your strengths, and the support you may need before classes begin.
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.
Curriculum
A structured journey to mastery
| Week | Focus Area | What You'll Master |
|---|---|---|
| W1 | Production AI Foundations & Structured Outputs | L1 recap, production AI mindset, prompt discipline, structured outputs, provider APIs, and the difference between a demo and a dependable service |
| W2 | Vector Databases & Retrieval Quality | Embeddings, vector stores, chunking, metadata design, indexing strategies, and retrieval-quality trade-offs |
| W3 | RAG Pipeline Development & Evaluation | RAG architecture, retrieval optimization, context assembly, citation-aware responses, and evaluation basics |
Where this course can lead
These are examples of roles, responsibilities, or directions this course can help you grow toward.
Frequently Asked Questions
Completion of Data Science & Machine Learning (TR-02) or equivalent is required. You should already be comfortable with Python, pandas, basic model training and evaluation, notebook workflow, and reading technical documentation. Before Day 1, review train/test split, leakage, prompt basics, and transformer fundamentals, then complete the AI readiness assessment. All applicants complete Saarathi Gate Assessment and an advanced readiness review before enrollment.
Ready to start your journey?
Complete Saarathi Gate, confirm your level, and secure your seat for the upcoming batch.
Skills You'll Master
Included Support
Live interactive sessions with trainers
Small-batch feedback on your practical work
Course materials, recordings, and lab support
Personalized guidance after Saarathi Gate on pacing, practice focus, and support
Career guidance for portfolio presentation, interviews, and next-step planning