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With rapid advancements across generative AI, vision models, and applied ML, I’d love to hear perspectives from recruiters and engineers currently in the space:
Which specific skill sets are seeing the highest demand right now (e.g., applied LLM/VLM engineering, traditional ML, MLOps, or full-stack integration)?
How are hiring bars shifting for entry-level vs. experienced AI roles?
What types of projects or proof-of-work actually stand out on a candidate's resume today?.
Right now, the strongest demand is for applied AI/LLM engineering and full-stack AI integration. In particular, companies are looking for engineers with hands-on experience building production-grade LLM applications, RAG pipelines, AI agents, VLMs, vector databases, and model/API integrations.
MLOps and AI infrastructure are also seeing strong demand, especially for deploying, monitoring, scaling, and optimizing AI workloads in cloud environments. Traditional ML remains relevant, but the market is increasingly favoring engineers who can combine AI/ML expertise with software engineering and production deployment skills.
From a recruiting perspective, the most requested combination is often Python + LLM/GenAI + RAG/Agents + cloud + MLOps + full-stack integration.