AI Engineering & Machine Learning Course in Nepal, LLMs, RAG, Agents, Fine-Tuning, Model Deployment & ML

Saarathi's 12-week AI Engineering and Machine Learning course is built to take someone from Python and classical machine learning all the way to a deployed, production-grade AI system, closing with a dedicated ML module. Every phase builds on the one before it, and every tool is introduced because a real AI engineering job requires it, not because it looks impressive on a syllabus.

Why AI Engineering Skills Are Valuable in Nepal and Globally

• AI engineering roles are being created faster than the talent pool can fill them globally. Engineers who can ship LLM-powered products are in short supply.
• Nepal's growing software industry is increasingly working on AI features for international clients and products. Local engineers with production-grade AI skills are positioned to lead those projects rather than outsource them.
• The transition from data science or backend development into AI engineering is faster than starting from zero, and Saarathi's course is structured to be the bridge regardless of which direction you are coming from.
• Building a portfolio of production AI work, RAG systems, agents, evaluation pipelines, directly unlocks remote and international opportunities.

What You Learn in Saarathi's 12-Week AI Engineering Program

The course is structured across four phases that mirror the actual AI engineering learning path: foundations and classical ML, LLM API engineering, RAG and agent systems, and production deployment with ML.

• Python and machine-learning foundations: Python for AI from scratch, NumPy, pandas, HuggingFace datasets, then a genuine classical machine-learning block, supervised models (linear and logistic regression, decision trees, random forests, gradient-boosting awareness), unsupervised k-means, train/validation/test discipline, cross-validation, the full metric set (accuracy, precision, recall, F1, ROC-AUC, RMSE, MAE), overfitting and regularisation, feature engineering and scikit-learn Pipelines, followed by deep learning and transformer architecture intuition.
• LLM API engineering and context engineering: OpenAI (with Structured Outputs strict mode), Anthropic Claude (Messages API, prompt caching, Extended Thinking) and Groq APIs, reasoning models (GPT-5-class reasoning (Thinking / o-series), DeepSeek R1 / V3-series) with thinking-budget tradeoffs, prompt engineering, the broader discipline of context engineering (curating every token the model sees), multi-modal inputs (text and image), Realtime / voice API awareness, cost optimization and an AI Gateway layer (OpenRouter / Portkey) for routing and spend control.
• RAG systems: vector databases (Pinecone, ChromaDB, pgvector, with Qdrant / Weaviate / Milvus awareness), modern embedding models (text-embedding-3, voyage-3.5, Cohere embed-v3, bge-m3, Matryoshka), chunking, hybrid retrieval (BM25 + dense) plus a reranking stage (Cohere Rerank, bge-reranker), LangChain (LCEL), LlamaIndex, agentic RAG and GraphRAG / LightRAG awareness with cost-based routing, and OpenAI Responses API + Conversations API vs custom RAG trade-offs (the Assistants API is removed 26 August 2026).
• Agent systems: tool use, function calling, agent orchestration with LangGraph (the prebuilt agent, with the legacy AgentExecutor noted as deprecated), building and consuming MCP (Model Context Protocol) tool servers that plug into Claude Desktop / Cursor / ChatGPT Desktop, agentic memory layers (Mem0 hands-on, with Zep + Graphiti temporal knowledge graph, Letta / MemGPT and LangMem at awareness), agent context engineering (compaction, note-taking, sub-agent isolation, just-in-time retrieval, tool-result clearing), the supervisor pattern for multi-agent workflows, plus awareness comparison against the OpenAI Agents SDK, the Claude Agent SDK, Deep Agents and Computer Use / browser agents.
• Production and ML: FastAPI for serving AI endpoints, Docker containerisation, MLflow experiment tracking, RAGAS plus Promptfoo / Braintrust / OpenAI Evals, LangSmith / Langfuse / Helicone / Arize Phoenix observability, CI/CD for AI systems, and cost monitoring through per-feature AI Gateway dashboards.
• Model fine-tuning: LoRA and QLoRA fine-tuning with synthetic data generation, Ollama and llama.cpp for local inference, vLLM and TGI awareness for production self-host, when fine-tuning outperforms prompt engineering.

Why Saarathi Academy for AI Engineering Training in Kathmandu

• This is a genuine basics-first program. Week 1 starts with Python from the fundamentals, not a refresher for people who already know it. Wherever you are in your Python journey, Week 1 builds the foundation the rest of the course depends on.
• AI tools are used as pair-coding partners from Day 1. GitHub Copilot, ChatGPT, and Cursor are integrated into the workflow because that is how real AI engineers actually work.
• The capstone is a production deployment, not a notebook. Your project must be a working, containerised, evaluated AI system that you can demonstrate and explain in an interview.
• Small batches mean your code and architecture decisions get reviewed by an instructor with real AI engineering experience, not just auto-graded on test cases.

Career Outcomes

This course targets Junior AI Engineer, Applied ML Engineer, and AI Integration Developer roles.