How do you vet AI engineers and Machine Learning specialists?

Our technical screening protocol ensures that every candidate presented can immediately contribute to your engineering deliverables and production workloads. We begin by reviewing their production track record, GitHub repositories, and published papers or models on platforms such as Hugging Face.

For specialized roles like a Senior AI/ML Engineer, our technical screening dives into Python and C++ proficiency, deep framework experience in PyTorch and TensorFlow, and practical mastery of model quantization, distributed training, and low-latency inference optimization.

Beyond core algorithms, we examine production readiness. For an MLOps Engineer, we evaluate their ability to automate CI/CD pipelines, prevent data and model drift, and orchestrate containerized microservices on Kubernetes across cloud providers like AWS, Azure, and Google Cloud.