Production LLM & Agentic AIEngineering Master Program

This program is engineered for Python developers, software engineers, AI/ML engineers, data scientists, full-stack developers, and technical professionals transitioning into modern production AI.

If you want to move beyond surface-level prompting and build production-grade Agentic systems, fine-tuned LLMs, and cloud-native AI pipelines, this program is specifically designed for you.

Instructor: Mian Ahmad Basit.

No. The roadmap starts with Python, math, ML, deep learning, and NLP foundations before moving into transformers, LLM fine-tuning, RAG, agents, security, and cloud deployment.

Yes. The program focuses on production Python, FastAPI services, Dockerized systems, vector databases, agent orchestration, guardrails, observability, and cloud deployment patterns.

The stack includes OpenAI GPT-4o and o-series models, Claude 3.5 Sonnet, DeepSeek-R1 and V3, Meta LLaMA 3.1/3.3, Google Gemini, Hugging Face models, Ollama, and vLLM.

Yes. You cover hybrid RAG, Self-RAG, Corrective RAG, Adaptive RAG, ColPali multimodal RAG, Neo4j GraphRAG, vector search, reranking, retrieval security, and evaluation.

Yes. The curriculum includes LangGraph, PydanticAI, MCP, A2A protocols, tool calling, supervisor agents, state checkpointing, human-in-the-loop workflows, and agent observability.

Yes. You learn SFT, LoRA, QLoRA, DoRA, DPO, GRPO, ORPO, quantization, synthetic datasets, PEFT workflows, and open-weight model deployment.

Yes. Security topics include prompt injection defense, NVIDIA NeMo Guardrails, Microsoft Presidio PII masking, LlamaFirewall, OWASP Top 10 for LLMs, RBAC, and private VPC deployment.

Yes. Deployment topics include Docker, Kubernetes, Helm, GitHub Actions, vLLM, SGLang, llama.cpp, AWS EKS, GCP, Azure, VPS hosting, and production observability.

Yes. You learn self-hosted n8n workflow automation, webhooks, FastMCP tool nodes, agent workflows, and enterprise automation patterns.

Yes. The curriculum covers ViT, CLIP, SigLIP, DINOv2, LLaVA, Qwen-2-VL, VLMs, image understanding, multimodal retrieval, and video/document processing.

Yes. The program includes 2 years of dedicated LMS access with recordings, repositories, notebooks, slide decks, quizzes, and periodic content updates.

You build production RAG pipelines, multi-agent systems, secure AI APIs, fine-tuning notebooks, vector database systems, n8n automations, guardrail-enabled assistants, and cloud-deployed AI services.

Yes. The program includes career and business guidance for AI consulting, Upwork and LinkedIn positioning, proposal templates, pricing models, retainers, and AI agency workflows.

Yes. Full-stack developers can use this program to move from app development into AI SaaS, agentic automation products, RAG platforms, and cloud-native AI systems.

Yes. ML engineers can deepen their skills in transformer internals, fine-tuning, model serving, LLMOps, evaluation, observability, RAG security, and production deployment.

The curriculum includes LangSmith, Langfuse, Logfire, RAGAS, Inspect AI, Pydantic Evals, lm-eval, token budgeting, semantic caching, tracing, and score-gated deployment.

The master program is designed as a deep 7-8 month engineering specialization with a complete progression from foundations to production AI architecture.

Yes. The page content describes direct guidance, live weekend sessions, code review checkpoints, architecture blueprints, and mentorship from Mian Ahmad Basit.

The program includes an official verified certificate of completion from the instructor for students who complete the required learning and project milestones.

Beginners can join if they are willing to follow the Phase 0 foundations seriously. The program starts from Python and AI foundations, but it is designed to move into advanced production engineering.

The end goal is to help learners move from toy demos to shipping secure, evaluated, observable, cloud-deployed AI systems for jobs, freelancing, consulting, and product development.