Name: Full Stack AI Engineer 2026 - Generative AI & LLMs III
Availability: InStock

What You'll Learn

Design and build production-ready generative AI systems using Large Language Models (LLMs), transformers, embeddings, and modern AI architectures.
Implement Retrieval-Augmented Generation (RAG) pipelines to ground LLMs in external knowledge, reduce hallucinations, and enable enterprise-grade AI application
Develop autonomous agentic AI systems using tool calling, multi-step reasoning, memory, and human-in-the-loop controls.
Create full-stack LLM applications by integrating FastAPI backends, streaming chat interfaces, frontend UX patterns, and stateful memory management.
Optimize AI systems for cost, latency, and scalability using token optimization, caching strategies, model selection tradeoffs, and load management techniques.
Evaluate and monitor LLM outputs using human and automated evaluation methods to ensure accuracy, relevance, and faithfulness.
Apply security, safety, and governance best practices by implementing guardrails, output filtering, policy-based controls, and responsible AI framework

Requirements

Basic programming knowledge (Python preferred, but not mandatory at an expert level)
General understanding of APIs or web applications (helpful, not required)
Curiosity about AI and willingness to build hands-on projects

Software engineers and full-stack developers who want to integrate LLMs and generative AI into real applications
Aspiring AI engineers looking to build job-ready skills in LLMs, RAG, agentic AI, and AI system design
Data engineers, data scientists, and ML engineers who want to move from model training to end-to-end AI system development
Backend and API developers interested in building LLM-powered services, chat systems, and AI-driven workflows
Product engineers and technical founders who want to design scalable, reliable AI-powered products
Students and recent graduates preparing for roles in AI engineering, applied AI, or full-stack AI development

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