Generative AI Engineering
Go from developer to production AI engineer in 12 weeks.
- Duration
- 12 weeks
- Duration
- Sessions
- 18
- Sessions
- Labs
- 9
- Labs
- Projects
- 3
- Projects
What You'll Be Able To Do
After completing this course, you will confidently:
- Explain transformer architecture, attention mechanisms, and how large language models generate text
- Design effective prompt strategies including few-shot, chain-of-thought, and structured output formatting
- Build production RAG pipelines with document ingestion, chunking, embedding, and retrieval from vector databases
- Fine-tune foundation models using LoRA and QLoRA for domain-specific tasks with custom datasets
- Implement LLMOps practices including evaluation metrics, automated testing, and cost monitoring
- Design autonomous AI agents with tool use, planning, memory, and multi-step reasoning capabilities
- Evaluate AI system quality using RAGAS metrics, golden datasets, and human feedback loops
- Deploy generative AI applications with proper guardrails, rate limiting, and observability
What You'll Build
Real portfolio projects that showcase your skills to employers.
RAG-Powered Knowledge Assistant
Build a knowledge assistant that ingests company documentation, chunks and embeds content, stores vectors in Pinecone, and answers questions with source citations. Includes evaluation with RAGAS and a Streamlit demo interface.
Interview value:
RAG systems are the number-one AI architecture hiring managers ask about. This project demonstrates end-to-end RAG development.
Fine-Tuned Domain Model
Fine-tune an open-source LLM for a specific domain task (code review, medical summarization, or legal extraction). Includes dataset curation, LoRA training, evaluation against the base model, and deployment.
Interview value:
Fine-tuning shows deep understanding of how models work internally — a differentiator from developers who only use API calls.
Multi-Agent Orchestration System
Design a multi-agent system where specialized agents collaborate to complete complex tasks. Includes a planner agent, researcher agent, writer agent, and reviewer agent with shared memory and human-in-the-loop approval.
Interview value:
Agentic AI is the frontier of LLM applications. This project shows you can architect complex autonomous systems with safety controls.
Course Curriculum
12 weeks of structured, hands-on learning.
1Transformer Architecture & LLM Internals
- Attention mechanism — self-attention, multi-head attention, positional encoding
- Transformer encoder-decoder architecture and decoder-only models
- Tokenization — BPE, SentencePiece, token limits and context windows
- Model families — GPT, LLaMA, Mistral, Claude, Gemini
2Prompt Engineering Mastery
- Zero-shot, few-shot, and chain-of-thought prompting
- System prompts, role assignment, and persona design
- Output formatting — JSON mode, function calling, structured extraction
- Prompt templating, versioning, and A/B testing strategies
3Embeddings & Vector Databases
- Text embeddings — dense vectors, semantic similarity, and model selection
- Vector similarity search — cosine distance, approximate nearest neighbors
- ChromaDB and Pinecone — indexing, metadata filtering, namespaces
- Embedding quality evaluation and domain-specific fine-tuning
4RAG Pipeline Architecture
- RAG architecture — retrieval, augmentation, generation, and evaluation
- Document loading — PDFs, HTML, Markdown, and structured data
- Chunking strategies — fixed, recursive, semantic, and parent-child
- Retrieval optimization — re-ranking, hybrid search, query expansion
5Advanced RAG Patterns
- Multi-index RAG — routing queries to domain-specific indexes
- Contextual compression and document summarization chains
- Conversational RAG — memory management and follow-up questions
- RAG evaluation with RAGAS — faithfulness, relevance, and completeness
6Fine-Tuning Foundation Models
- When to fine-tune vs prompt engineering vs RAG
- Dataset curation — quality, format, and size requirements
- LoRA and QLoRA — parameter-efficient fine-tuning
- Training loop — learning rate, epochs, loss monitoring
7Fine-Tuning Evaluation & Deployment
- Model evaluation — perplexity, BLEU, ROUGE, and task-specific metrics
- Comparing fine-tuned vs base model performance
- Model quantization for efficient serving (GGUF, GPTQ, AWQ)
- Serving fine-tuned models with vLLM and Ollama
8LLMOps & Production Practices
- LLM application lifecycle — development, evaluation, deployment, monitoring
- Automated testing — golden datasets, regression suites, boundary tests
- Cost optimization — caching, model routing, token budgets
- Monitoring — latency tracking, token usage, error rates, drift detection
9AI Agent Architecture
- Agent design patterns — ReAct, plan-and-execute, tree-of-thought
- Tool design — API integrations, database queries, code execution
- Memory systems — short-term, long-term, and episodic memory
- Agent evaluation — task completion, efficiency, and safety
10Multi-Agent Systems
- Multi-agent orchestration — supervisor, graph, and swarm patterns
- Agent communication protocols and shared state
- Human-in-the-loop approval and intervention points
- Guardrails — output validation, content filtering, cost limits
11Deployment & Guardrails
- Deploying AI applications with FastAPI and Docker
- Streaming responses with Server-Sent Events
- Input/output guardrails — PII detection, content policy, token limits
- Rate limiting, authentication, and API key management for AI services
12Capstone Project & Interview Preparation
- End-to-end capstone project execution and presentation
- AI engineering interview patterns — system design, RAG architecture
- Common pitfalls in AI system design and how to avoid them
- Portfolio presentation and resume optimization for AI roles
Hands-On Labs Included
You build these yourself — guided exercises with real tools, not passive demos.
Prompt Engineering Lab — Complex Tasks
Docker Lab2 hours
Vector Database Setup & Semantic Search
Docker Lab2.5 hours
Build a Production RAG Pipeline
Docker Lab3 hours
Fine-Tune a Model with LoRA
Docker Lab3 hours
Build a ReAct Agent with Tools
Docker Lab2.5 hours
Multi-Agent Orchestration System
Docker Lab3 hours
Who Is This For?
Career Switchers
Moving from another domain into tech? The structured curriculum and real-world projects bridge the gap between theory and what employers actually look for.
Working Professionals
Already in tech and looking to upskill? Deepen your expertise with production-grade labs and system design patterns used at top companies.
Ideal If You Are:
- Software developers with 1+ years of experience wanting to specialize in AI
- Career switchers from data science or analytics moving into AI engineering
- Backend engineers who want to build AI-powered products
- Technical leads evaluating AI integration strategies for their teams
Prerequisites
- At least one year of programming experience in any language
- Basic Python proficiency (functions, classes, HTTP requests)
- Understanding of REST APIs and JSON data formats
- An OpenAI API key (setup guided in Week 1)
Career Support Included
We don't just teach you — we help you land the job.
Mock Interviews
Practice with real-world interview scenarios. Get feedback on technical depth, communication, and problem-solving approach.
Resume Review
One-on-one review sessions to craft a resume that highlights your projects, skills, and achievements the right way.
Portfolio Coaching
Guidance on presenting your course projects as professional portfolio pieces that stand out to hiring managers.
LinkedIn Optimization
Tips and templates to optimize your LinkedIn profile so recruiters find you and reach out.
Learn from Industry Practitioners
Our instructors are working professionals who build production systems daily. They bring real-world experience, battle-tested patterns, and the kind of practical insight that textbooks can't teach.
Course Details
| Format | Live Online |
|---|---|
| Duration | 12 weeks |
| Schedule | 18 sessions |
| Batch Size | Max 15 students |
| Certificate | Yes, on completion |
| Lab Setup | Docker-based (runs on your laptop) |
| Price | Enquire for pricing |
Frequently Asked Questions
Will I get a job after completing this program?
Generative AI engineering is the fastest-growing specialization in software development. Companies are actively hiring for RAG engineers, AI backend developers, and LLM platform engineers. Our curriculum covers exactly what these roles require. While we cannot guarantee placement, the skills and projects are directly aligned with market demand.
Do I need experience with machine learning or AI?
No prior AI or ML experience is required. We teach transformer architecture and LLM concepts from fundamentals. However, you do need at least one year of general programming experience and basic Python skills.
How much will the OpenAI API cost during the course?
We design labs to minimize costs. Most labs cost under $1 in API calls. We also teach you to use open-source models (Ollama, Hugging Face) that run locally at zero cost. Total API spend for the course is typically under $15.
Is this different from the Python AI Backend Engineering course?
Yes. This course goes deep into AI concepts — transformers, fine-tuning, multi-agent systems, and LLMOps. The AI Backend course focuses on backend engineering with AI integration. Choose this if you want to specialize in AI; choose AI Backend if you want to be a backend engineer who builds AI features.
Do I need a GPU?
No. All labs run on CPU. For fine-tuning, we use parameter-efficient techniques (LoRA, QLoRA) that work on standard hardware. Google Colab free tier provides GPU access for larger experiments.
What if I miss a live session?
All sessions are recorded and available on the student portal within 24 hours. The instructor and TAs are available on Slack for questions between sessions.
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Ready to Start Your Generative AI Engineering Journey?
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