Skills You NEED for AI Engineering in 2026
In this Hindi-language presentation, Akansha Yadav outlines the essential skill set for AI engineers in 2026. She begins by emphasizing the importance of understanding foundational and formation models such as GPT‑like, BERT‑style, and other large‑scale language models. She then moves to deep‑learning performance techniques, highlighting few‑shot learning, high‑performance training, and the use of modern libraries like JAX. The talk covers core NLP pipelines – tokenization, transformer pre‑training phases, and the role of scientific‑transformer variants. Yadav stresses practical fine‑tuning: selecting business‑relevant hyper‑parameters, achieving reliable response generation, and aligning models with company objectives. Finally, she introduces the ecosystem of LLM frameworks and agent‑oriented tools (LangChain, DeepSpeed, etc.), the necessity of API integration, and hands‑on experience with GPT‑based services. Throughout, she encourages a hands‑on, experiment‑driven approach to mastering these competencies.
