Hire senior AI engineers who ship in your stack
Senior AI engineers who build retrieval, agents and evaluation pipelines in your repos and cloud, working the same hours as your team.
By the Ryz Labs team · Updated October 2026
Hiring AI engineers through Ryz gets you senior engineers who have shipped AI features to production, not just prototypes. Only the top 1% of candidates we interview make it through. They join your standups, work in your repos and cloud, and keep the same hours as your US team.
What our AI engineers work on
Our AI engineers sit between your product and the models. They turn a promising demo into a system that handles real traffic, real data permissions and real failure modes. Typical work looks like this:
- Retrieval-augmented generation over internal documents, with chunking, hybrid search and reranking tuned to your content, built on Postgres with pgvector, OpenSearch or a managed vector store.
- Agents and tool-calling workflows that read from and write to your systems through typed function schemas, using the Anthropic and OpenAI APIs, LangGraph or plain Python orchestration.
- Document processing pipelines that classify, extract and route PDFs, emails and forms, with structured outputs validated by Pydantic.
- Evaluation harnesses: golden datasets, LLM-as-judge scoring, regression suites in CI and dashboards your product team can read.
- Voice and chat assistants with streaming responses, guardrails, human handoff and conversation logging.
- Cost and latency work: prompt caching, model routing, batching and smaller models where they hold up on your evals.
Skills we vet for
- Production Python and TypeScript. Async I/O, typed interfaces, tests and clean service boundaries. AI code is still code.
- LLM APIs in depth. Tool use, structured outputs, streaming, context window management, rate limits and retries across Anthropic, OpenAI and Azure OpenAI.
- Retrieval design. Embedding choice, chunking strategy, metadata filters, hybrid BM25 plus vector search and cross-encoder reranking.
- Evaluation discipline. Building test sets before tuning prompts, measuring faithfulness and answer quality, and catching regressions when a model version changes.
- Agent architecture. When to use a fixed workflow versus an open-ended agent loop, how to bound tool access and how to make runs replayable.
- Data access and security. Row-level permissions in retrieval, PII redaction, secrets handling and keeping customer data in your cloud account.
- Cloud deployment. Containers on AWS or Azure, queues for long-running jobs, and observability with OpenTelemetry or tools like Langfuse.
- Product judgment. Knowing when a rules engine or a classic classifier beats an LLM, and saying so.
How we vet AI engineers
Our recruiters source candidates who have AI work in production, not only course projects. Our in-house ARC system ranks the pipeline so recruiters spend their time on the strongest profiles. Candidates then complete structured NTRVSTA AI interviews built around real AI engineering scenarios. Recruiters review every candidate before and after that interview, and we send you a curated shortlist. AI scores are advisory. People make the decisions, and you meet the engineers before anyone starts.
Sample interview topics
- Your RAG assistant answers confidently from an outdated policy document. Walk through how you would find the cause and fix it at the retrieval, ranking and prompt layers.
- Design an evaluation suite for a support agent that calls three internal APIs. What goes in the golden set, and what do you score automatically versus by hand?
- A model provider ships a new version and your extraction accuracy drops. How do you detect it, pin versions and roll forward safely?
- How do you enforce document-level permissions so a user never sees retrieved content they are not allowed to read?
- Your agent loops on a failing tool call and burns tokens. What limits, fallbacks and tracing would you add?
Ways to hire AI engineers
| Option | Best for | Trade-offs |
|---|
| Freelance marketplace | A short prototype or a single integration | You do the vetting. Availability and continuity vary, and AI experience is hard to verify from a profile. |
| Staffing or recruiting agency | Filling a defined role with a resume pipeline | Screening is often keyword-based. Few agencies can test whether someone has run an eval or debugged retrieval. |
| In-house recruiting | Long-term core AI leadership | Slow and expensive for a scarce skill. Your team spends hours interviewing before anyone ships. |
| Ryz Labs staff augmentation | Adding senior AI engineers to an existing product team | You manage day-to-day priorities. Best when you already have a roadmap and a team to join. |
| Ryz Labs AI pod team | Building a new AI system end to end in your cloud | A dedicated pod with a tech lead, ML, backend and data engineers. More scope than one hire, so it is planned up front. |
Ryz is not the right fit if you want a self-serve marketplace for hourly gigs, engineers in European or Asian time zones, or a proprietary AI platform you license rather than build.
Why hire AI engineers from Latin America
AI work moves in tight loops. You change a prompt, rerun the evals, look at failures and decide what to try next. That loop breaks when your engineers are asleep during your workday. Our engineers work on US business hours, so a product manager can flag a bad answer in the morning and see a fix and a new eval run the same afternoon.
Latin America also has deep senior backend and data talent, which matters because most AI engineering is backend and data engineering with a model in the middle. Our engineers are fluent in English and used to working directly with US product, security and compliance teams.
Related roles
FAQ
What is the difference between hiring one AI engineer and an AI pod team?
One engineer joins your existing team and works your backlog. An AI pod team is a dedicated group of senior engineers, often around seven people with a tech lead, ML engineer and backend engineers, that builds a specific AI system in your cloud and repos, with weekly demos.
Which models and clouds do your AI engineers work with?
Most of our work runs on AWS or Azure with Anthropic and OpenAI models, GitHub for code and Postgres for data. Engineers adapt to the stack you already have rather than bringing their own platform.
How much does it cost to hire AI engineers through Ryz?
$7,000 to $15,000 per engineer per month, scoped per team. Before you sign, you get a plan, a price and the names of the people who would do the work.
What hours do they work, and how do they fit into our team?
They work on US business hours, including New York hours. Ryz engineers work on your team and report to your leads. Talk to us to scope your team.
Can AI engineers work with sensitive or regulated data?
Yes. They build in your cloud account under your access controls, so data stays where your security team wants it. We serve companies across industries, including financial services, healthcare and logistics.
Questions we didn't answer? Email info@ryzlabs.com.