Contingency recruiting
A recruitment model where the company pays the recruiter only when it hires a candidate the recruiter introduced. There is no fee for the search itself. Boecia Talent works this way: the fee depends on the role and is quoted before any work starts. See how hiring with Boecia Talent works.
Vetted shortlist
A short list of candidates the recruiter has already checked for a specific role, so the company interviews only people who fit. At Boecia Talent, vetted means we speak with each engineer about the role before we share their profile.
Replacement guarantee
A promise that the recruiter finds a new candidate without a new fee if the hire leaves within a set period. Boecia Talent replaces a hire who leaves within 90 days for free.
Independent contractor
A self-employed professional who signs a services agreement with a company, invoices for the work and handles their own taxes and social security. See contractor or employee in Latin America.
Employer of record (EOR)
A company that legally employs a worker in their own country on behalf of another company. It runs local payroll, taxes, social security and the benefits the law requires, while the worker works with the client company day to day. Deel and Remote are examples. See contractor or employee in Latin America.
Nearshore hiring
Hiring people in a nearby country whose working hours overlap with yours. For US companies this usually means Latin America, where many cities are within two hours of New York time; for European companies, Eastern Europe. See Latin America and US time zones.
LLM engineer
An engineer who builds product features on top of large language models: retrieval over your own data, prompts, tools and agent steps, evaluation, cost and latency, guardrails and the backend code around them. See hiring LLM engineers.
Retrieval-augmented generation (RAG)
A way to build LLM features where the system first retrieves relevant passages from your own documents or data and then gives them to the model, so answers are based on your content. It is a core part of an LLM engineer's work.
LLM evaluation
Measuring whether an LLM feature gives good answers, with a test set of real questions, expected answers or grading rules, and checks that run before every release. It is how a team knows a change made the feature better.
MLOps
The work of getting machine learning models into production and keeping them working: packaging and serving models, deployment and rollback, monitoring and retraining. See hiring MLOps engineers.
Data engineer
An engineer who builds and runs the pipelines that bring data into one place, models it in the warehouse and makes sure it arrives on time and is correct. Dashboards, reports and models all depend on that work. See hiring data engineers.
Backend engineer
An engineer who builds the server side of a product: APIs, databases, queues, integrations with other services, and the code that keeps them secure, fast and reliable. See hiring backend engineers.
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