CV/CVC AI Engineer 2026

## Lộ trình Ôn thi & Chuẩn bị cho AI Engineer - VietinBank ### 📖 GIAI ĐOẠN 1: NỀN TẢNG (Tuần 1-2) #### Python & Backend Fundamentals **Tài liệu tham khảo:** - Python: "Fluent Python" hoặc "Python Crash Course" - FastAPI: [fastapi.tiangolo.com/tutorial](https://fastapi.tiangolo.com/tutorial) - Docker: "Docker Deep Dive" by Nigel Poulton **Thực hành bắt buộc:** ```python # 1. Xây dựng một FastAPI service đơn giản # 2. Viết Dockerfile để containerize service # 3. Implement REST API với authentication ``` **Kiến thức cần nắm:** - Python advanced (async/await, decorators, type hints) - RESTful API design principles - Docker basics (build, run, docker-compose) - Git workflow #### Machine Learning Fundamentals **Tài liệu tham khảo:** - Andrew Ng's ML Course (Coursera) - Module 1-4 - Scikit-learn documentation **Thực hành:** - Classification, clustering, recommendation basics - Model evaluation metrics --- ### 📖 GIAI ĐOẠN 2: LLM & GENAI CORE (Tuần 3-4) #### Large Language Models Fundamentals **Tài liệu tham khảo:** 1. "Generative AI with Large Language Models" - DeepLearning.AI/AWS Course 2. "Prompt Engineering Guide" - promptengineeringguide.com 3. Attention Is All You Need (Transformer paper) **Thực hành bắt buộc:** ```python # 1. Sử dụng OpenAI API / Gemini API # 2. Experiment với different prompt techniques: # - Zero-shot # - Few-shot # - Chain-of-thought # - Constitutional AI from openai import OpenAI client = OpenAI() response = client.chat.completions.create( model="gpt-4", messages=[ {"role": "system", "content": "You are a banking assistant."}, {"role": "user", "content": "Explain RAG in simple terms"} ] ) ``` **Concepts cần master:** - Transformer architecture (attention, tokens, context window) - LLM APIs (OpenAI, Anthropic, Google, local models) - Prompt engineering techniques - Hallucination và mitigation strategies - Token computation & cost optimization #### Embeddings & Vector Search **Tài liệu tham khảo:** - "What is Embedding?" - OpenAI documentation - FAISS documentation - ChromaDB quickstart **Thực hành:** ```python # Vector embedding example với ChromaDB import chromadb from chromadb.utils import embedding_functions sentence_transformer = embedding_functions.SentenceTransformerEmbeddingFunction("all-MiniLM-L6-v2") client = chromadb.Client() collection = client.create_collection( name="banking_docs", embedding_function=sentence_transformer ) collection.add( documents=["Interest rate policy document...", "Customer service procedure..."], ids=["doc1", "doc2"] ) results = collection.query( query_texts=["What is the interest rate for savings accounts?"], n_results=2 ) ``` --- ### 📖 GIAI ĐOẠN 3: RAG PIPELINE (Tuần 5-6) #### Retrieval-Augmented Generation **Tài liệu tham khảo:** 1. RAG Paper (Lewis et al., 2020) 2. LangChain Documentation - RAG tutorials 3. LlamaIndex Documentation - RAG guides **RAG Architecture Components:** ``` ┌─────────────┐ ┌──────────────┐ ┌────────────────┐ │ Documents │───▶│ Chunking & │───▶│ Embedding │ │ (Raw) │ │ Preprocessing│ │ Generation │ └─────────────┘ └──────────────┘ └───────┬────────┘ │ ▼ ┌─────────────┐ ┌──────────────┐ ┌────────────────┐ │ LLM │◀───│ Prompt │◀───│ Vector Store │ │ (Generate) │ │ Augment │ │ (Retrieve) │ └─────────────┘ └──────────────┘ └────────────────┘ ``` **Thực hành toàn diện:** ```python # Complete RAG Pipeline với LangChain from langchain.document_loaders import PDFLoader from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.chat_models import ChatOpenAI from langchain.chains import RetrievalQA # 1. Load documents loader = PDFLoader("banking_policy.pdf") documents = loader.load() # 2. Chunking splitter = RecursiveCharacterTextSplitter( chunk_size=1000, chunk_overlap=200 ) chunks = splitter.split_documents(documents) # 3. Embedding & Store embeddings = OpenAIEmbeddings() vectorstore = Chroma.from_documents(chunks, embeddings) # 4. Retrieval & QA llm = ChatOpenAI(model="gpt-4", temperature=0) qa_chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=vectorstore.as_retriever() ) result = qa_chain({"query": "What is the minimum deposit?"}) ``` **Key RAG Strategies cần biết:** - Chunking strategies (fixed size, recursive, semantic) - HyDE (Hypothetical Document Embeddings) - Query expansion & reformulation - Re-ranking (Cohere reranker) - Hybrid search (keyword + vector) #### RAG Evaluation **Metrics cần biết:** - **RAGAS**: Faithfulness, Answer Relevancy, Context Precision/Recall - **BLEU, ROUGE**: Text similarity (baseline) - **Custom metrics**: Citation accuracy, hallucination rate --- ### 📖 GIAI ĐOẠN 4: AI AGENTS (Tuần 7-8) #### Agentic AI & Orchestration **Tài liệu tham khảo:** 1. LangChain Agents documentation 2. "Building Effective Agents" - Anthropic guidelines 3. ReAct paper (Reasoning + Acting) **Core Agent Patterns:** ```python # Simple ReAct Agent với LangChain from langchain.agents import AgentType, initialize_agent, Tool from langchain.tools import DuckDuckGoSearchRun search = DuckDuckGoSearchRun() tools = [ Tool( name="Search", func=search.run, description="Useful for searching current information" ) ] agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True ) agent.run( "What is VietinBank's latest interest rate policy? " "Compare with ACB and VCB." ) ``` **Patterns cần master:** 1. **ReAct (Reasoning + Acting)**: Think → Act → Observe loop 2. **Plan-and-Execute**: Plan first, then execute sequentially 3. **Multi-Agent Systems**: Specialized agents collaborate 4. **Tool Integration**: Connect agents with external systems **Workflow Automation:** - N8N basics (được nhắc đến trong JD) - Integration patterns với enterprise systems --- ### 📖 GIAI ĐOẠN 5: MLOPS & PRODUCTION (Tuần 9-10) #### LLM Deployment & Monitoring **Thực hành:** ```python # Monitoring với LangSmith hoặc custom logging import json from datetime import datetime class GenAIMonitor: def __init__(self): self.metrics = [] def log_interaction(self, query, response, latency, tokens): self.metrics.append({ "timestamp": datetime.now().isoformat(), "query": query, "response_length": len(response), "latency_ms": latency, "tokens_used": tokens, "feedback": None }) def get_stats(self): return { "total_requests": len(self.metrics), "avg_latency": sum(m["latency_ms"] for m in self.metrics) / len(self.metrics), "total_tokens": sum(m["tokens_used"] for m in self.metrics) } ``` **Production Considerations:** - **Latency optimization**: Caching, async processing, model distillation - **Cost management**: Token budgeting, model routing - **Security**: Input validation, output filtering, PII detection - **Compliance**: Audit logging, data retention --- ### 📖 GIAI ĐOẠN 6: DỰ ÁN THỰC TẾ & ÔN TẬP (Tuần 11-12) #### Project Ideas để Build Portfolio **Project 1: Internal Knowledge Base Chatbot** - RAG với company documents (mock data) - Include citation và source tracking - Deploy với FastAPI + Docker **Project 2: Banking Agent Assistant** - Multi-agent: balance inquiry, transaction history, loan calculator - Integration với mock banking API - Include error handling và escalation **Project 3: Document Processing Pipeline** - Auto-classify và extract info từ loan applications - Use OCR + LLM + validation rules #### Ôn tập Interview Prep **Coding Practice:** - LeetCode: 10-15 problems (focus: strings, arrays, graphs) - Python-specific: decorators, generators, comprehensions - SQL: joins, aggregations, window functions **System Design Practice:** - Design a RAG system for banking documents - Design a customer service chatbot - Design an AI agent for loan processing **Mock Interview:** - Practice với friend hoặc sử dụng Pramp/Interviewing.io - Time yourself: 45 phút cho system design --- ### 📚 TÀI LIỆU THAM KHẢO TỔNG HỢP **Courses & Certifications:** | Nguồn | Link | Chi phí | |-------|------|---------| | DeepLearning.AI LLM Course | coursera.org | ~$49/tháng | | LangChain Academy | academy.langchain.com | Miễn phí | | Google ML Engineering | cloud.google.com/training | Miễn phí | **Communities & Newsletters:** - The Batch (DeepLearning.AI) - Weekly newsletter - Import AI (Nathan Benaich) - Weekly - Hugging Face Daily Papers - r/MachineLearning, r/LocalLLaMA **Vietnamese AI Community:** - Vietnam AI Alliance - VietnamML Community - AI Vietnam Group (Facebook)