In this video, the speaker shares a comprehensive roadmap for becoming an AI engineer in 2026, based on over a decade of experience in AI and real-world client projects. He defines the role of an AI engineer as a software engineer who builds production-ready systems using pre-trained AI models and APIs, focusing on applied AI rather than training models from scratch. The roadmap is structured around six core pillars, each with specific learning outcomes designed to build practical skills and prepare learners for real-world AI engineering roles.
The first pillar emphasizes mastering Python, the dominant programming language in AI, along with understanding its ecosystem, including development environments, version control with Git, testing, debugging, and managing dependencies. The speaker recommends starting with OpenAI’s API and Python SDK to learn how to authenticate, send requests, and handle responses from large language models (LLMs). Additionally, prompt engineering is introduced as a crucial skill for instructing LLMs effectively. By the end of this phase, learners should be able to build and run small, well-structured Python projects locally.
The second pillar focuses on AI system design principles, teaching learners how to conceptualize and design AI systems before coding. This includes understanding various applications of LLMs, combining deterministic logic with AI for effective solutions, and studying software design patterns like chain of responsibility and strategy patterns. The speaker also highlights the importance of exploring agent frameworks such as LangChain and Pentic AI, and encourages reimplementing simplified versions of these frameworks to gain deeper mastery. Sketching cognitive architectures to visualize data flow and AI integration is also a key skill in this phase.
The third and fourth pillars cover backend development and retrieval-augmented generation (RAG). Learners are guided to turn local prototypes into scalable backend services using FastAPI, Pydantic, Docker, and PostgreSQL, while understanding asynchronous programming and event-driven architectures. The RAG pillar teaches how to connect AI systems to external data sources using embeddings, vector databases, and advanced retrieval techniques to improve context relevance and reliability. Building ingestion pipelines and evaluating retrieval performance are essential skills here, enabling AI systems to access and use external information effectively.
The final two pillars address monitoring, evaluation, and deployment of AI applications. Monitoring involves using tools like Langfuse for tracing LLM calls, managing prompt versions, and capturing performance metrics, while setting up evaluations ensures continuous improvement and regression testing. Safety guardrails and error tracking with tools like Sentry are also covered to maintain reliability and security. Deployment focuses on cloud platforms, containerization, HTTPS configuration, environment management, and CI/CD pipelines to serve AI applications in production. The speaker stresses the importance of building and deploying real projects, documenting work, and showcasing at least three complete systems to demonstrate practical skills for job interviews. Overall, the roadmap encourages hands-on building over endless learning, emphasizing that core AI engineering principles remain stable despite rapid model improvements.
