Staff augmentation

Hire AI engineers

AI engineering is the fastest-growing role in tech, and the talent pool has not caught up. Mavric's CTO-led vetting finds engineers who ship production AI systems, not just notebook prototypes.

Production AI engineering, not science fair demos

Most candidates with 'AI engineer' on their resume have fine-tuned a model in a Jupyter notebook and deployed it nowhere. The engineers your team needs are different: they build LLM-powered features that handle real traffic, design RAG pipelines that return accurate results at scale, and integrate embeddings into existing product architectures without rewriting everything. The demand-to-supply ratio for qualified AI talent is 3.2:1. Finding someone who can actually ship is harder than it looks.

Mavric screens for applied AI engineering. Every candidate we present has built production systems on top of foundation models, worked with vector databases and retrieval pipelines, and understands the operational side: latency budgets, token cost management, prompt versioning, evaluation frameworks, and monitoring. You interview one strong candidate instead of sorting through dozens of people who watched a tutorial.

Why teams hire AI engineers through Mavric

01

CTO-led AI vetting

Your fractional CTO understands the difference between someone who completed an online course and someone who shipped a production RAG pipeline. Technical assessments cover real-world scenarios: retrieval accuracy tuning, prompt engineering under constraints, model selection trade-offs, and integration with existing codebases. You get candidates who have done the work, not just studied it.

02

Applied AI, not research

Mavric finds engineers who build products on top of models, not researchers publishing papers. Our AI engineers integrate LLMs into SaaS platforms, build retrieval-augmented generation systems, implement embedding pipelines, and deploy inference endpoints that handle production load. They write application code, not just experiment scripts.

03

Senior AI talent at $6K-$8K/mo

The average US AI engineer salary is $206K per year. Mavric's global talent pool across Pakistan, Africa, and Latin America gives you senior AI engineers at 40-50% less than US contractors, with full timezone overlap. If a placement does not meet expectations within 30 days due to performance or timezone issues, we replace them at no extra cost.

Technical expertise

Mavric AI engineers work across the modern AI and ML stack. From foundation model APIs to deployment infrastructure, they bring production experience in the tools your team already uses or plans to adopt.

PythonPyTorchTensorFlowLangChainLlamaIndexOpenAI APIAnthropic APIHugging FaceRAG pipelinesVector databasesPineconepgvectorMLflowFastAPIDockerKubernetes
Frequency

Mavric engineers built Frequency's audio ad platform to handle 10,000 requests per minute with sub-300ms response times. The system processes and serves audio ads at scale using React, Node.js, and data pipeline architecture that mirrors the kind of high-throughput AI inference work our engineers handle today.

Read the case study

Frequently Asked Questions

Machine learning engineers typically focus on training and optimizing models from scratch: feature engineering, model architecture, training pipelines, and evaluation metrics. AI engineers focus on building applications on top of existing foundation models. They integrate LLMs, design retrieval systems, manage prompt chains, and ship user-facing AI features. There is overlap, but the day-to-day work is different. Most companies hiring today need AI engineers who can build with models, not train them.

Ready to Hire Your Next Engineer?

Talk to a CTO