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AI Engineer Production Track: Deploy LLMs & Agents at Scale

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The AI Engineer Production Track moves beyond simple chatbot prototypes and dives deep into the high-stakes world of enterprise-grade AI deployment. This comprehensive program is meticulously designed to bridge the gap between local development and production-ready infrastructure. You will master the complexities of deploying Large Language Models and sophisticated Agentic systems across leading cloud providers such as AWS, GCP, Azure, and Vercel. The curriculum places a heavy emphasis on MLOps, ensuring that your AI architectures remain scalable, observable, and secure in a real-world environment. Participants will gain hands-on experience with modern industry standards, including Amazon Bedrock, SageMaker, and advanced multi-agent orchestration. By integrating automation via Terraform and GitHub Actions, this course empowers developers to manage end-to-end lifecycles—from development and testing to continuous delivery. Whether you are aiming to implement robust RAG pipelines or complex Agentic loops with guardrails, this track provides the technical foundation needed to build AI solutions that businesses can trust, monitor, and scale under heavy production loads.

AI Coder: Vibe Coder to Agentic Engineer in 3 Weeks

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This comprehensive program is designed to take developers, tech enthusiasts, and product creators from casual 'vibe coders' to elite agentic software engineers. Throughout the curriculum, participants transcend simple prompting to orchestrate advanced, autonomous AI developer agents. The course provides deep hands-on experience with industry-standard tools like Cursor, Claude Code, GitHub Copilot, and emerging frameworks like OpenClaw and the Model Context Protocol (MCP). Students master building complete, scalable applications without traditional manual coding bottlenecks, moving through four extensive real-world projects: a personalized AI digital twin website, an interactive Kanban project manager, a SaaS legal document assistant, and a live trading workstation capstone. Beyond individual tools, learners explore complex multi-agent architectures, sub-agents, swarms, and orchestrators that replicate entire engineering departments. By integrating these systems with modern development pipelines like Jira, GitHub, and specialized Ralph Loops, this course empowers anyone to deliver production-grade software at unprecedented speeds, transforming how engineering teams build in the AI era.

AI Builder: Create Agents, Voice Agents & Automations in n8n

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This comprehensive program from Ligency empowers aspiring AI builders, product managers, and automation enthusiasts to master the cutting-edge landscape of autonomous agents using n8n v2.0. By bridging the gap between low-code ease and enterprise-grade capability, the course provides a practical, step-by-step framework to design, deploy, and scale intelligent workflows. Learners will dive deep into real-world projects, including building an autonomous financial agent that tracks market data and structures portfolios, crafting interactive voice agents using ElevenLabs and Twilio for real-time customer interactions, and engineering multi-agent systems driven by the Model Context Protocol (MCP) and FireCrawl for automated lead generation. Additionally, the curriculum covers vital concepts like Agentic Retrieval Augmented Generation (RAG) with Supabase vector databases, webhook handling, and seamless LLM integration (OpenAI, Gemini, Anthropic). Perfect for those looking to eliminate repetitive administrative tasks and build robust AI systems without extensive coding, this course serves as the ultimate launchpad into the future of business automation and intelligent agent design.

AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents

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The AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents course, curated by Ligency, represents a highly comprehensive blueprint for developers aiming to transition into the rapidly evolving field of Generative AI engineering. This structured, project-driven program guides learners through the transition from standard software engineering to crafting production-grade artificial intelligence systems. Over an intensive path, students tackle eight real-world practical applications, establishing a robust portfolio that demonstrates capability in advanced AI concepts. The curriculum bridges theoretical foundations with cutting-edge methodologies. It covers everything from intelligent web scraping and multi-modal customer support agents with advanced function-calling to high-performance tasks like optimizing code speed and building retrieval-augmented generation (RAG) knowledge-bases. Furthermore, learners master fine-tuning open-source models using techniques like LoRA and QLoRA to challenge multi-billion-parameter frontier models in specialized tasks. By diving into autonomous multi-agent environments, this curriculum equips participants to architect complex workflows where independent AI agents collaborate to solve intricate problems. This track is ideal for software engineers, data scientists, and tech professionals aiming to build viable, high-performance LLM products and stay at the forefront of the Generative AI revolution.