Klyver recruits and technically screens Data and AI talent in Mexico and LATAM for U.S. and Canadian companies. We charge a one-time placement fee — the client hires directly, as a contractor, through their own entity, or via an EOR of their choice.
The data and AI profiles in highest demand.
From data pipelines to production integrations with LLMs.
Data Engineers
Pipelines, ETL/ELT, Airflow, dbt, Spark.
Analytics Engineers
Data modeling, dbt, modern warehouses.
BI
Looker, Power BI, Tableau — dashboards for business decisions.
AI/ML Engineers
ML models in production, MLOps, feature stores.
LLM / GenAI
LLM integrations, RAG, agents, fine-tuning.
Data Science
Statistical modeling, experimentation, A/B testing.
A recruiting process, not an outsourcing template.
Klyver doesn't sell a development factory or run your payroll. We find and screen the professional; you decide how to hire them.
You define the role
Role, stack, seniority and compensation range. No volume minimums — start with a single position.
Sourcing and technical screening
We search across Mexico and LATAM, filter for real experience, and run technical screening before presenting anyone.
You receive evaluated profiles
A shortlist with concrete evidence of what the candidate can actually do, not just a resume.
You hire, directly
As a contractor, through your own entity, or the EOR you already use. Klyver doesn't sit in the employment relationship.
Placement fee
A one-time payment when you hire, with no retainer and no recurring monthly billing per candidate.
Frequently asked questions
Screening looks for evidence of real work: production integrations with LLMs, RAG architectures running in production, hallucination evaluation and mitigation, and cost/latency trade-off decisions — not just conceptual knowledge of the topic.
Yes. MLOps sits at the intersection of Data/AI and DevOps — we evaluate experience with training pipelines, model versioning, drift monitoring, and production deployment.
A Data Engineer is evaluated more on pipelines, orchestration, and data infrastructure. An Analytics Engineer is evaluated on data modeling, quality, and the semantic layer for business consumption. They're different screenings even though the tools overlap.
Yes. It's a common case: companies that need their first Data Engineer or Analytics Engineer to lay the foundation, without hiring a full team from day one.
We also recruit these profiles.
Software Engineers
Full Stack, Backend, Frontend, Python, Java, .NET, Node.js, React.
View specialty →Looking for your next Data or AI hire?
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