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🧠 Bootcamp

AI Engineering Bootcamp

Build, Deploy and Operate AI Systems in Production.

A comprehensive bootcamp covering the full AI engineering lifecycle — LLM fundamentals, RAG pipeline design, fine-tuning, private AI deployment, MLOps and production AI operations. For engineers who build and operate AI systems.

Duration12 Weeks

Total Hours96 Hours

LevelIntermediate

FormatOnline + Offline

CertificateYes

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Delivery Format

Train How You Learn Best

💻 Online — Live Instructor-Led

Live sessions via Zoom with a ZippyOPS practitioner. 4 sessions per week, all recordings provided. Ask questions in real time and get code reviewed live.

🏢 Offline — Chennai Lab Sessions

In-person at ZippyOPS Chennai labs. Mon–Fri batches. Lab machines provided. Direct hands-on access to instructors throughout every session.

Who Should Attend

Is This Bootcamp Right for You?

✅ This bootcamp is for you if…

Software engineers wanting to build production AI applications
Data scientists wanting to get AI systems to production reliably
DevOps engineers tasked with deploying and operating LLMs
Engineers in regulated industries needing private, on-premises AI

📋 Prerequisites

Python proficiency — comfortable with classes, async, APIs
Basic understanding of machine learning concepts
Familiarity with Docker and cloud infrastructure
Some experience with REST APIs and databases

Full Curriculum

What You'll Learn — Week by Week

LLM Foundations

Weeks 1–2

▾

How large language models work — transformer architecture intuition
Model families — GPT-4, Claude, Llama, Mistral, Gemini and their trade-offs
Tokenisation, context windows, temperature, top-p and output control
Prompt engineering — zero-shot, few-shot, chain-of-thought and role prompting
API integration — OpenAI, Anthropic and Hugging Face APIs with Python
Evaluation fundamentals — how to measure LLM output quality systematically
Lab: Build a document summarisation tool using GPT-4 with structured output and quality evaluation

RAG — Retrieval-Augmented Generation

Weeks 3–4

▾

RAG architecture — why retrieval matters and how it extends LLM knowledge
Document processing — chunking strategies, metadata extraction and preprocessing
Embedding models — OpenAI, Cohere, sentence-transformers and model selection
Vector databases — Qdrant, Weaviate, Pinecone and Chroma compared
Retrieval strategies — dense retrieval, sparse retrieval (BM25) and hybrid search
Advanced RAG — reranking, query rewriting, multi-query and HyDE
LlamaIndex and LangChain — building and orchestrating RAG pipelines
Lab: Build a production-grade RAG system over a 10,000-document knowledge base with hybrid search and reranking

AI Application Design & Agentic Systems

Week 5

▾

AI application architecture — when to use LLMs, when not to
Agentic systems — ReAct, tool use and multi-step reasoning
Tool and function calling — connecting LLMs to APIs, databases and code execution
Multi-agent architectures — orchestrator and worker patterns
Lab: Build an agentic system that autonomously researches a topic and produces a structured report

Private & On-Premises AI Deployment

Weeks 6–7

▾

When cloud AI fails — data residency, GDPR, HIPAA and air-gap requirements
Open-source model landscape — LLaMA 3, Mistral, Phi-3, Gemma and Qwen
Ollama — running LLMs locally and on-premises
vLLM — high-throughput LLM inference with continuous batching
GPU infrastructure — CUDA, NVIDIA drivers and multi-GPU serving
Deploying private RAG pipelines in air-gapped environments
RBAC and access control for private AI systems
Lab: Deploy LLaMA 3 70B on-premises with vLLM, build a RAG pipeline and expose it as a secured internal API

Fine-Tuning & Model Adaptation

Week 8

▾

When fine-tuning makes sense vs RAG vs prompt engineering
LoRA and QLoRA — parameter-efficient fine-tuning without full model training
Dataset preparation — data quality, formatting and domain-specific curation
Fine-tuning LLaMA and Mistral with Unsloth and Hugging Face PEFT
Evaluation after fine-tuning — measuring domain improvement and regression
Lab: Fine-tune Mistral 7B on a domain-specific dataset using QLoRA and evaluate against the base model

AI Infrastructure & MLOps

Weeks 9–10

▾

MLflow for experiment tracking, model registry and versioning AI applications
CI/CD for AI — automated evaluation pipelines before model promotion
Model serving at scale — BentoML, Seldon Core and NVIDIA Triton
Cost management for AI workloads — GPU spot instances, batching and caching
AI observability — monitoring LLM latency, token usage and quality drift
Lab: Build a full MLOps pipeline for an LLM application with automated evaluation and production promotion

AI Safety, Security & Responsible AI

Week 11

▾

LLM security vulnerabilities — prompt injection, jailbreaking and data leakage
Input/output guardrails — NeMo Guardrails, Llama Guard and custom validators
Sensitive data handling — PII detection and redaction in AI pipelines
Bias and fairness evaluation — measuring and mitigating model bias
Responsible AI deployment — transparency, auditability and human oversight
Lab: Implement a full security and safety layer blocking injection attacks, PII leakage and harmful outputs

Capstone Project

Week 12

▾

Build a full AI application — internal knowledge assistant, document analysis pipeline or domain chatbot
RAG pipeline with vector database and hybrid search
Private on-premises deployment using vLLM with RBAC
MLOps pipeline with automated evaluation and promotion
AI security layer with guardrails, PII detection and audit logging
Live technical review and demo with ZippyOPS AI engineers

On Completion

Earn Your ZippyOPS Certificate

🎓

ZippyOPS Certified AI Engineer (ZCAIE)

Validates practical knowledge of end-to-end AI engineering — LLM deployment, RAG pipelines, fine-tuning and production MLOps — through a capstone building a production AI application.

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