Building AI Products? Here's What to Look for in Real AI Engineering Talent
AI is no longer a side project; it’s moving to the core of products, platforms, and competitive advantage. Whether it’s deploying LLMs, designing ML infrastructure, or integrating AI into critical workflows, one thing is clear:
You need engineers who can ship, not just prototype.
As a recruiter focused exclusively on AI talent, our conversations with Engineering Managers and CTOs who are facing the same challenge is starting to grow: the talent gap between AI experimentation and real-world delivery is growing.
So what should you look for in AI Engineers, and where do you find them?
The Modern AI Engineer: Not Just a Data Scientist with Code
AI Engineering is now a distinct discipline. The best candidates combine:
ML foundations (model training, optimisation, evaluation)
Production mindset (CI/CD for ML, MLOps, latency and reliability)
Software engineering principles (clean, testable, scalable code)
Tool fluency (PyTorch, TensorFlow, MLflow, LangChain, Hugging Face, etc.)
Cloud-native deployment (Docker, K8s, serverless, GPU infra)
Collaboration with product, data, and infra teams
Whether they’re building custom LLM pipelines, ML models for edge devices, or RAG-powered internal search, these engineers are designing for scale, privacy, safety, and speed.
Why Great AI Engineers Are Hard to Find
You’ve probably seen it plenty of candidates talk a good game, but few have:
Deployed models in production
Built end-to-end pipelines
Worked with real-world data constraints
Balanced experimentation with engineering discipline
Navigated ethical, compliance, and bias challenges in AI systems
Many teams waste months hiring candidates who are strong in theory but unproven in delivery.
How I Help Engineering Leaders Build Real AI Capability
As a recruiter deeply embedded in the AI & Data space, I focus on pre-vetting engineers and architects who have delivered production-grade AI systems across industries like fintech, healthcare, and SaaS.
I’ve built a passive talent network of:
AI Engineers with hands-on experience in NLP, computer vision, and LLMs
MLOps Engineers who build scalable, monitored pipelines
AI Architects who can lead technical design and cross-functional delivery
If you're hiring for AI roles and want more than just buzzwords on a resume, I can help you connect with candidates who are already solving the problems you're trying to tackle.
Any questions or requirements, please do not hesitate to reach out to
Gino Lancaster , Chief AI Talent Officer
