The demand for AI talent has never been higher, and within that market, generative AI specialists are among the most sought-after professionals in the industry. For organisations looking to build internal AI capabilities or augment their teams with specialised expertise, the decision tohire Generative AI Developers requires careful consideration. This article outlines what to look for, what to ask, and how Custom Generative AI Development shapes the skill requirements.
Core Technical Skills
When you hire Generative AI Developers, the foundation of the evaluation is technical depth. Look for demonstrable experience with large language models — not just API consumption, but a genuine understanding of how transformer architectures work, how models are trained and fine-tuned, and how they can be adapted to specific domains and tasks.
Practical skills in data engineering are equally essential. Generative AI models are only as good as the data they are trained or grounded on. Developers who understand data pipelines, cleaning methodologies, embedding strategies, and vector database management are far more valuable than those who can only call an API.
Experience with Custom Generative AI Development
There is a significant gap between using off-the-shelf AI tools and delivering Custom Generative AI Development. Custom development requires the ability to evaluate foundation models against specific requirements, design fine-tuning pipelines, implement retrieval-augmented generation (RAG) architectures, and build evaluation frameworks that measure model performance against business-specific criteria.
When screening candidates for Custom Generative AI Development roles, ask to see examples of projects where they adapted a foundation model to a specific domain. What data did they use? How did they measure success? What challenges did they encounter and how did they overcome them? These questions will quickly reveal the depth of real-world experience.
MLOps and Production Experience
A developer who can build impressive generative AI prototypes is valuable. A developer who can take those prototypes to production — with proper monitoring, logging, versioning, and reliability engineering — is invaluable. When you hire Generative AI Developers for enterprise contexts, prioritise candidates with MLOps experience: familiarity with tools like MLflow, Weights & Biases, or similar platforms, and an understanding of how to manage the full model lifecycle in production.
Soft Skills and Domain Understanding
Generative AI development does not happen in isolation. Developers must work closely with business stakeholders, data engineers, UX designers, and compliance teams. Communication skills, the ability to translate technical concepts for non-technical audiences, and a genuine curiosity about the business domain are important differentiators when you hire Generative AI Developers.
Conclusion
Hiring for generative AI is a high-stakes decision. The right developers will accelerate your Custom Generative AI Development journey and build solutions that deliver lasting value. Take the time to evaluate candidates rigorously, prioritise production experience alongside model knowledge, and look for professionals who are as excited about your business problems as they are about the technology.
