The Enterprise AI Engineer
Build secure, scalable, and production-ready AI solutions for enterprise. Master LLMs, RAG, fine-tuning, agents, and deploy enterprise-grade AI systems.
Created by Baljeet Dogra
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.
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
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
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
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
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
- Lifetime access to all materials
- Pre-recorded video lectures
- Community forum access
- Certificate upon completion
Cohort-Based
- Everything in Self-Paced
- Live weekly sessions
- Direct instructor access
- Peer collaboration
- Real-time Q&A
Premium
- Everything in Cohort-Based
- 1-on-1 mentorship sessions
- Career coaching
- Guaranteed portfolio review
- Job placement assistance
- Bonus: AI Consultant Track included
Ready to Become an Enterprise AI Engineer?
Join the next cohort and build secure, scalable AI systems in 20 weeks.