Everyone lists "AI" on their résumé now. Our recruiters can tell someone who has fine-tuned a model in production from someone who has watched a tutorial. AI hiring goes wrong when the person screening cannot.

Roles we place

Machine Learning Engineers
AI / ML Research Scientists
Data Scientists
MLOps & ML Platform Engineers
NLP & LLM Engineers (RAG, fine-tuning, evaluation)
Computer Vision Engineers
AI Solutions Architects
AI Product Managers
Data Engineers for AI/ML pipelines
Heads of AI / ML (leadership)

Python PyTorch TensorFlow LLMs Hugging Face LangChain RAG Vector DBs MLOps CUDA / GPU

How we staff Artificial Intelligence

AI people are hard to find, expensive, and change jobs often, so the search needs a clear brief and steady follow-up. Here is how a role goes from an idea to a signed offer.

1. Discover

We work out the use case (an LLM application, computer vision, a recommender, an MLOps platform) and whether you need research depth, applied engineering, or someone to take models into production.

2. Source

We go to ML and research communities and to engineers we already know who run models in production. We do not rely on whoever answers a job post this week.

3. Screen

Technical checks on model and system design, the math and its trade-offs, and evidence of production deployment. Notebooks and Kaggle scores alone do not pass.

4. Deliver

A short list of screened candidates, then help with the offer and onboarding. Good AI candidates often take another offer within days, so we keep the process moving.

Screening & compliance

Most of AI screening is separating production experience from hype. Depending on the role, that includes:

Technical interviews on ML system design, model evaluation, and MLOps practices
Checking that claimed production experience is from deployed systems rather than academic or hobby projects
Review of portfolios, papers, GitHub, and detailed references
Background checks and, for sensitive data or models, support for data-governance, IP, and security requirements
Work-authorization confirmation and sponsorship guidance, since many AI candidates are outside the US

Best-fit engagement models

Most AI hiring fits one of these. We will tell you which one suits the work and the timeline:

Contract & Temp →Model builds, POCs, and specialized projects. Contract-to-Hire →De-risk a scarce, high-cost hire. Direct Hire →Core ML roles and AI leadership.

Why Pheenix for Artificial Intelligence

Your recruiter knows the difference between fine-tuning and pre-training, and between research and production work, so the screening filters people out instead of passing buzzwords along. We keep the search moving, tell you plainly where your pay sits in an overheated market, and only send people who can explain their work to your senior engineers.

Artificial Intelligence & Machine Learning staffing