Talent — AI & ML Engineers
Hire AI and ML engineers who ship production models, not just prototypes.
Zenaide places pre-vetted machine learning engineers, LLM specialists, MLOps engineers, and applied AI talent — on contract, contract-to-hire, or permanent basis. Engineers who build AI systems that work in production, at scale.
Every company needs AI talent. Almost nobody can hire it fast enough.
The demand for production-grade AI and ML engineers has outpaced supply across every industry. Companies aren't just competing for researchers — they need engineers who can design ML systems, build training pipelines, deploy models to production, and iterate based on real-world performance.
Finding AI talent who can bridge the gap between research and production engineering is one of the hardest technical hiring challenges in 2026. Zenaide maintains an active pipeline of applied AI and ML engineers — from LLM specialists building GenAI features to MLOps engineers keeping production models reliable at scale.
Tools and platforms.
Capabilities.
Roles we place.
How we vet AI and ML talent.
Technical deep-dive
Assessment covering ML system design, model training and evaluation, deployment patterns, and production ML architecture — not just Kaggle scores.
Production experience validation
We verify hands-on experience shipping ML models to production — training pipelines, inference optimization, monitoring, and iteration at scale.
Tool and framework assessment
Stack-specific evaluation across PyTorch/TensorFlow, cloud ML services, MLOps tooling, and the LLM/GenAI ecosystem your team actually uses.
Cross-functional evaluation
AI engineers work across data, engineering, and product teams. We assess communication clarity, collaboration style, and ability to translate research into shipped software.
Why Zenaide for AI hiring.
We vet for production, not papers. The AI talent market is full of researchers and bootcamp graduates. Our vetting focuses on engineers who have actually shipped ML models to production — training pipelines, inference infrastructure, monitoring, and iteration.
We understand the AI stack. Our recruiters know the difference between fine-tuning an LLM and building a RAG pipeline, between a data scientist who builds dashboards and an ML engineer who builds inference services. That specificity means better matches and fewer wasted interviews.
We move at AI speed. The AI landscape shifts monthly. Companies that wait three months to hire ML engineers fall behind permanently. We maintain an active pipeline so you see strong candidates in days, not quarters.
Your AI team starts here.
Tell us the roles, the stack, and the timeline. We'll present pre-vetted AI and ML candidates who build production systems — not science projects.