Hire AI engineers who can turn models into useful products.
Targeted AI and machine learning recruiting for U.S. companies. We calibrate each search around applied ML, generative AI, model integration and production requirements, with a focus on the candidate’s actual contribution to AI projects.
What we look for.
AI job titles can cover research, model development, application engineering and infrastructure. The search should reflect the particular AI capability your team is trying to build.
Technical relevance
Experience with applied machine learning, deep learning, LLM applications or AI infrastructure, matched to the intended use case.
Model and product delivery
Evidence of developing, integrating, evaluating or deploying AI systems, with clarity about what the candidate personally built.
Data and evaluation
Understanding of model performance, data quality, evaluation methods, reliability and the limitations of AI-generated results.
From role intake to shortlist.
Define
Establish the intended AI application, the model and data environment, the expected deliverables and whether the role is research-oriented or production-focused.
Map
Identify relevant ML, applied AI, LLM or AI platform backgrounds according to the stage and complexity of your project.
Validate
Explore model selection, experimentation, evaluation, application integration and the candidate’s experience addressing real implementation challenges.
Shortlist
Present professionals whose AI experience aligns with your use case, technical requirements and expected project responsibilities.
AI talent for your next product or initiative.
HireVista helps U.S. employers identify AI engineers for applied machine learning, intelligent applications and AI-enabled product development. We shape the search around the practical distinction between developing models and delivering working AI solutions.
Hiring several engineering roles?
A broader technical RPO or staff augmentation model may make more sense than running isolated searches.
Hiring AI engineers.
Should we hire an AI engineer or a machine learning engineer?
It depends on the work. AI engineering roles may focus on integrating models into products, while ML engineering roles often emphasize model development, training, deployment and ongoing evaluation. We can help define the profile around your intended deliverables.
Can you recruit engineers with generative AI and LLM experience?
Yes. Searches can be calibrated around LLM applications, model integration, retrieval-augmented generation and related experience where these capabilities are relevant to the position.
What should we look for when hiring an AI engineer for a production application?
Consider the candidate’s experience with evaluation, deployment, reliability, data quality and the operational constraints of the application. It is also important to understand which parts of the solution they personally designed or delivered.
How do we assess AI project experience during recruitment?
Define the project requirements and ask candidates to explain the problem, their contribution, the methods they used and how they evaluated the result. Your technical interview can then explore the underlying implementation and technical trade-offs.
Looking for AI engineers with relevant project experience?
Tell us what you are building, the AI capabilities you need and the technical requirements for the position. We’ll align the search with your project.