Enterprise-Focused

The Enterprise AI Engineer (Zero-to-Hero)

Build secure, scalable, and production-ready AI solutions for enterprise. Master LLMs, RAG, fine-tuning, agents, and deploy enterprise-grade AI systems.

20
Weeks
5
Phases
4+
Milestone Projects
100%
Enterprise-Ready

Course Philosophy: "Build to Learn"

This is not a theoretical research course. It is a practical engineering course. By the end, you will not just understand how Transformers work; you will have deployed secure, private, and scalable AI agents for business use cases.

Why This Course?

Enterprise-focused training for building production-ready, secure, and scalable AI solutions.

Enterprise Security

Learn OWASP Top 10 for LLMs, guardrails, PII redaction, and build secure AI systems that meet enterprise compliance requirements.

Production-Ready

Build scalable microservices, implement LLMOps, deploy to cloud with CI/CD, and create systems that handle enterprise workloads.

Real-World Projects

Build 4+ milestone projects including Policy Bot, Brand Voice fine-tuning, Automated Analyst agent, and a complete enterprise capstone.

Open Source & Private

Move beyond expensive APIs. Learn to run Llama, Mistral, and other open-source models locally with quantization and fine-tuning.

AI Agents & Automation

Master function calling, tool use, multi-agent systems, and build autonomous agents that can plan, act, and collaborate.

Bonus: AI Consultant Track

Learn business case development, ROI calculation, legal compliance (EU AI Act), and how to sell AI strategy, not just code.

5-Phase Curriculum

From foundations to enterprise capstone: Build secure, scalable AI systems.

01

Phase 1: The Foundations (Weeks 1-4)

Master Python for AI, data handling, APIs, and cloud deployment basics.

  • Advanced Python: Async programming, type hinting, decorators
  • Data handling: Pandas, Polars, JSON/Parquet, APIs (REST, GraphQL, WebSockets)
  • Math intuition: Vectors, embeddings, neural networks (high-level)
  • Docker & Cloud: Containerization, AWS/Azure basics, CI/CD with GitHub Actions
  • Milestone Project 1: Deploy Dockerized FastAPI app with prediction model
02

Phase 2: The Core AI Engineering Stack (Weeks 5-8)

Build applications using Large Language Models and RAG systems.

  • Prompt Engineering: Zero-shot, few-shot, Chain-of-Thought, ReAct patterns
  • LLM APIs: LiteLLM router, structured output with Instructor/Pydantic
  • RAG Systems: Vector databases (Pinecone, ChromaDB), chunking strategies, LlamaIndex
  • Advanced Retrieval: Hybrid search, re-ranking, metadata filtering
  • AI Evaluation: RAGAS, DeepEval, metrics (faithfulness, precision, relevance)
  • Milestone Project 2: Policy Bot - RAG system with exact page citations
03

Phase 3: Enterprise Architecture & Open Source (Weeks 9-12)

Move to owned, private, and fine-tuned models for enterprise use.

  • Open Source Models: Llama 3, Mistral, Mixtral, Gemma
  • Local Inference: Ollama, vLLM, TGI, quantization (GGUF, AWQ)
  • Fine-Tuning: PEFT, LoRA, QLoRA, instruction dataset creation
  • LLMOps: LangSmith/Helicone tracing, cost tracking, semantic caching
  • AI Security: OWASP Top 10 for LLMs, guardrails, PII redaction, RBAC
  • Milestone Project 3: Brand Voice - Fine-tune model on company style
04

Phase 4: Agents & Complex Systems (Weeks 13-16)

Build systems that can take action, plan, and use tools.

  • Function Calling: LLM tool use, SQL integration, calculator connections
  • Agentic Frameworks: LangChain, LangGraph (state machines), CrewAI
  • Agent Loops: Planning → Acting → Observing → Reflecting
  • Multi-Agent Systems: Manager/Worker agents, task handoffs, collaboration
  • Multimodal AI: Vision (GPT-4o), Audio (Whisper, ElevenLabs), real-time voice bots
  • Milestone Project 4: Automated Analyst - Multi-agent web scraping & reporting
05

Phase 5: The Capstone (Weeks 17-20)

Build an end-to-end enterprise AI solution.

  • Architecture: Microservices (Frontend, Backend, Vector DB, Worker)
  • Security: Guardrails, PII redaction, compliance-ready
  • Deployment: AWS/Azure/GCP with full CI/CD pipeline
  • Evaluation: Dedicated dashboard showing accuracy scores
  • Examples: Legal Contract Reviewer, Medical Triage Chatbot, Internal Search System

Bonus Track: The AI Consultant

For those who want to sell the strategy, not just write the code.

The AI Business Case

  • • ROI Calculators: Token costs vs. Employee time savings
  • • Buy vs. Build: ChatGPT Enterprise vs. Custom Llama
  • • Feasibility: "Is this actually an AI problem?" checklist

Legal, Ethics & Compliance

  • • EU AI Act: Risk categories and compliance
  • • Copyright: Risks of generative image/code usage
  • • Bias: Detecting and mitigating bias in algorithms

Implementation Strategy

  • • Change Management: Training staff on AI tools
  • • Lock-in Risk: Model-agnostic architectures
  • • Vendor Assessment: Evaluating vector DBs and cloud providers

Prerequisites

Coding

Basic familiarity with any programming language (Logic, Loops, Functions)

Math

High-school level Algebra and Statistics

Hardware

Laptop with internet (NVIDIA GPU is a bonus, cloud options provided)

Choose Your Learning Path

Flexible options to fit your schedule and learning style.

Self-Paced

£799
  • Lifetime access to all materials
  • Pre-recorded video lectures
  • Community forum access
  • Certificate upon completion
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Most Popular

Cohort-Based

£1,999
  • Everything in Self-Paced
  • Live weekly sessions
  • Direct instructor access
  • Peer collaboration
  • Real-time Q&A
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Premium

£3,999
  • Everything in Cohort-Based
  • 1-on-1 mentorship sessions
  • Career coaching
  • Guaranteed portfolio review
  • Job placement assistance
  • Bonus: AI Consultant Track included
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Join the next cohort and build secure, scalable AI systems in 20 weeks.

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