About the role
The AI Engineer role consists of building product features on top of foundation models — Claude, GPT, Gemini — rather than training models from scratch: RAG pipelines, agent flows, document intelligence, AI features inside an existing product. The job is turning “we should add AI to this” into shipped, evaluated, cost-controlled software — with evals defining what “good enough” means, and latency and token spend treated as part of the spec. In 2026 this is the most-hired AI role by far, and the one most companies actually mean when they say they need “an AI person.”
Monthly rate
$5,500–$8,000/mo
All-in: contract, benefits, equipment, IP
Experience
10+ years engineering
3+ in LLM production
Location
Latin America
Argentina · Colombia · Mexico
Timezone
Full US overlap
Fluent English, onboarded in one week
Core stack
AI tools, daily
Verticals seen
What they own — and what they don't
What they own
- Build LLM-powered features end to end: RAG pipelines, chat interfaces, document intelligence, agent flows
- Design and run evals so output quality is measured, not vibes-checked
- Own prompt and context engineering — what the model sees, in what order, at what cost
- Integrate models with your real systems: databases, APIs, auth, existing product code
- Keep latency and token spend inside budget as usage scales
What they don't — and who does instead
- Train or fine-tune custom models at scale — that's an ML Engineer
- Design your company-wide AI strategy or pick use cases — that's an AI Solutions Architect
- Run GPU infrastructure and model serving platforms — that's MLOps
- Decide what the feature should be and when it ships — that's product management
Who hires this role, and for what
Series B+ startups adding AI to an existing product. They have product-market fit and engineers, but nobody who has shipped LLM features before. One senior AI Engineer unblocks the whole roadmap.
SaaS companies under competitive pressure. Their category is getting AI features from competitors quarterly. They need to ship credible AI, not a thin ChatGPT wrapper, and they need it this quarter.
Enterprises past the pilot stage. The POC worked in the demo and died in production. They hire AI Engineers to add evals, guardrails, and engineering discipline to something a consultancy left half-finished.
- 01
RAG over internal knowledge. Support docs, contracts, wikis, tickets — making a company's own information answerable, with citations and access control.
- 02
AI features inside an existing product. Summarization, extraction, classification, drafting — the features users now expect inside every SaaS.
- 03
First agentic workflows. Multi-step flows that read, decide, and act on real systems — usually starting with one well-guarded back-office process.
- 04
Rescuing a stalled AI pilot. Taking a demo that impressed the board and rebuilding it with evals, fallbacks, and cost control so it survives real users.
Work our engineers at this role have shipped
- Multi-tenant agentic platform with per-user OAuth credential isolation for a private-markets firm
- RAG pipeline over 100K internal docs for a mid-market SaaS
- MCP server catalog for a Claude/Cursor-native enterprise buyer
- Voice-first conversational agent behind a Fortune 500 support flow
Do you actually need an AI Engineer?
You do, if:
- You have a concrete AI feature on the roadmap and nobody who has shipped one to production
- Your team prototyped something with an LLM API and it's stuck at 'works in the demo'
- AI features exist but there are no evals — quality regressions ship silently
- Token costs are growing faster than usage and nobody owns the number
You probably don't, if:
- You need to train custom models on proprietary data — start with an ML Engineer instead
- You haven't picked a use case yet — an AI Solutions Architect will save you months of building the wrong thing
- You need someone to run discovery with a customer and ship on their systems — that's a Forward Deployed Engineer
Not sure which role fits? Tell us the problem instead of the title — we'll tell you what we'd actually staff, even if it's not this. If it is this: discovery call today, matched profiles in 48 hours, onboarded in a week.
Hire a Senior AI Engineer