An engineer leaning over a laptop while a colleague takes notes beside a chart pinned to the wall, in a bright office with plants and concrete walls

/AI Engineers

AI engineer outsourcing. Measured with evals, owned by you.

AI engineers who build LLM features, retrieval, agents and the evals that keep them honest, in your repositories and cloud accounts, with senior engineering oversight.

From prototype
to a feature you trust

Getting a model to give one good answer in a demo is easy. Getting it to answer well on your real data, every release, at a cost you can live with, is engineering work. That's the gap most AI projects fall into.

AI engineer outsourcing gives you a team that closes it. Teams are delivered through partner firms we select and manage, in Mexico and Colombia or in India, the Philippines and Egypt. You get one contract and one point of contact, with senior engineering oversight on top.

The work runs in your cloud accounts with your API keys. Code, prompts, eval sets and model artifacts are assigned to your company from the first commit.

What we deliver

LLM Features

Extraction, summaries, drafting and classification built into your product behind your own API.

Retrieval (RAG)

Chunking, embeddings, search and the prompts that use the results, so answers cite your own data.

Agents & Tool Use

Models that call your APIs, with clear limits on what they can do without a person approving.

Evals

Golden sets, scoring rules and regression evals that run in CI before every prompt or model change ships.

Model Selection & Fine-Tuning

Hosted and open models compared on your eval set, and fine-tuned only when the evals say it's needed.

MLOps & Data Pipelines

Deployment, monitoring, prompt and model versioning, rollback, and the pipelines that feed retrieval and evals.

How it works

Discovery

We learn the feature, the data it touches and what a good answer looks like to your product lead.

Shortlist & Interview

We screen AI, ML and data engineers for the work. You interview the shortlist and decide.

Access Setup

SSO accounts, repository access, least-privilege cloud roles and model API access through your keys.

First Slice & Eval

Within the first two weeks, a thin prototype runs against a first golden set, with results written up.

Delivery & Oversight

Senior engineers review the work, and eval results are reviewed with your product lead every week.

What an AI engineer does

An AI engineer turns a model into a working product feature. They choose the model, design prompts and tools, wire in your data, measure quality with evals and ship the result behind your API.

It's software engineering with one extra problem: the output changes from run to run, so unit tests alone can't tell you it works. The work usually covers several of these jobs:

Prototyping LLM features inside your product: extraction, summaries, drafting and classification.
Retrieval-augmented generation (RAG) over your documents and records.
Agents that call your APIs, with limits on what they can do alone.
Evals: golden sets, scoring rules and regression runs in CI.
Model selection and fine-tuning, judged on your own eval set.
MLOps: deployment, monitoring, versioning and rollback.
Cost and latency tuning: caching, batching and smaller models where they pass the evals.

AI engineers, software engineers and data annotators

Most good AI engineers started as software engineers. If the work is a general product or platform build with a little AI in it, a software development team is usually the better fit.

Data annotation is different again. Annotators label and rate examples by hand; AI engineers build the systems that learn from those labels or get scored against them. Many AI projects need both, and the two teams can share one eval set.

Software development teams Dedicated engineering teams for general product and platform work. Data annotation outsourcing Human labeling and response rating for training and eval data.

Evals decide what ships

A team without evals argues about examples. A team with evals argues about numbers it agreed on in advance. We set them up in the first weeks, before the feature grows.

That means a golden set of real inputs owned by your product lead, regression evals on every prompt, model or retrieval change, and a short note on each run. Cost and latency are tracked next to quality, so a better answer isn't ten times slower. Failing cases get reviewed live with product every week.

Keys, data and AI risk

AI work touches your data, your prompts and your model provider bill. It gets the same controls as production engineering, plus a few that are specific to models.

NIST's AI Risk Management Framework (AI RMF 1.0, January 2023) organizes the work into Govern, Map, Measure and Manage, and it's a useful shared reference with your security team. The OWASP Top 10 for LLM Applications names risks such as prompt injection, sensitive information disclosure and excessive agency, which the team should test for by name.

Work runs in your cloud accounts with your API keys, never a vendor's or an engineer's personal keys.
Least-privilege roles through your SSO, removed the day someone leaves.
Secrets in your secrets manager, never in code, prompts or notebooks.
Written rules for which data may appear in prompts, eval sets and training data.
Code, prompts, eval sets and model artifacts assigned to your company in the contract.

Hire AI engineers in Mexico, Colombia or offshore

Nearshore fits work that's still taking shape: early prototypes, new agent behavior and anything your product lead is still defining. Central Mexico stays on UTC-6 all year and Colombia on UTC-5, so a failing case can go on a call at 2 p.m. and the fix gets rerun before the day ends.

Offshore, in India, the Philippines and Egypt, fits well-specified work: expanding an eval suite to a written spec, data pipelines, batch jobs and integration backlogs, with overnight eval runs and written handoffs. Onshore costs most, then nearshore, then offshore. Many teams blend the two, with nearshore leads reviewing offshore work.

Nearshore AI engineers Mexico and Colombia for iterative AI product work in your day. Offshore AI engineers India, the Philippines and Egypt for well-specified AI work. Onshore vs nearshore vs offshore How the three delivery models compare on cost, hours and handoffs.

Not sure what to build yet?

A team is the right answer once you know the use case. If you're still deciding which process to automate or whether a model can do it at all, start with an AI integration engagement and staff the team after.

Our own AI work includes LLM-based data extraction in a healthcare workflow and a design assistant for modular construction. The case studies show how those were built.

AI integration services Help deciding what to build and putting models to work in your business. Case study: LLM data extraction in healthcare How LLM-based extraction fits a healthcare platform's workflow. Case study: AI design assistant An AI design assistant for modular building fabricators.

Questions to ask an AI engineering partner

Ask to see an eval report from past work. Client details can be removed, but the method should be clear.
Ask whose keys and accounts the work runs on. The only good answer is yours.
Ask who owns the prompts, eval sets and fine-tuned weights. The contract should assign them to you, including work by the partner firm's engineers.
Ask how they test for prompt injection and excessive agency. A partner who can't name the risks won't catch them.

Choose how
we run it

Nearshore · Mexico & Colombia

Nearshore AI engineers in your working day.

Nearshore AI engineers in Mexico and Colombia for LLM features, RAG, agents and evals: which work fits, time zones, security and how to run the team.

Offshore · Philippines, India & Egypt

Offshore AI engineers for well-specified work.

Offshore AI engineers in India, the Philippines and Egypt: which AI work fits, overnight eval runs, written handoffs, key and data security, and cost drivers.

Choose where
the work happens

Nearshore

Mexico & Colombia

Bilingual teams in US hours. The default for live, judgment-heavy work.

Offshore

Philippines, India & Egypt

Overnight, overflow and high-volume work in partner centers we manage and report on.

Onshore

On request

For roles that must be US-resident, arranged case by case.

Frequently asked questions

What is AI engineer outsourcing?

It's adding external AI engineers to build LLM features, retrieval, agents, evals and the pipelines behind them. The team comes through partner firms we select and manage, with senior engineering oversight, one contract and one point of contact. They work in your repositories and cloud accounts with your API keys, and everything they build is assigned to you.

How is an AI engineer different from a software engineer?

An AI engineer works with model output that changes from run to run. On top of normal engineering, they design prompts and tools, build retrieval over your data, choose and sometimes fine-tune models, and write evals that score quality on real examples. Most good AI engineers started as software engineers, so ask for both skill sets in the interview.

Can I hire AI engineers in Mexico through OTRO?

Yes. Nearshore teams work from Mexico and Colombia through partner firms we select and manage. Central Mexico stays on UTC-6 all year, within two hours of every mainland US time zone, and Colombia matches US Eastern in winter. We pick the country per team, based on the AI and data skills it needs first. You interview every engineer.

How much does AI engineer outsourcing cost?

It depends on seniority, the mix of AI, ML and data roles, and team size, so we don't publish rates. As an ordering, onshore costs most, then nearshore, then offshore. Remember the second bill too: model API and compute spend, which is why evals track cost next to quality. Tell us the roles you need and we'll send a written plan.

Can the team work with our customer data?

Yes, under rules you write before they start. Decide which data may go into prompts, eval sets and training data, and mask or remove personal data where the task allows. Keep everything in your own accounts, and approve any third-party tool in writing first. Your counsel should check what your customer contracts and privacy laws allow.

What happens to the code, prompts and models we build?

They're yours. The contract assigns code, prompts, eval sets, fine-tuned weights and other model artifacts to your company. Work lives in your repositories and cloud accounts from the first commit, so nothing sits on a vendor's systems. Ask your counsel to confirm the assignment covers the partner firm's engineers too, not only OTRO.

How is AI engineering different from data annotation?

Data annotation is people labeling examples: tagging text, drawing boxes, rating answers. AI engineering is building the system that uses those labels: the pipeline, the model, the retrieval and the evals. Many AI projects need both. Our data annotation team covers the labeling side, and the two teams can share one eval set.

Tell us what you're
building with AI.

Send us the feature, the data it touches and the hours your leads keep. We'll reply with a written plan: roles, a location for each and how the first two weeks would run.

Plan my AI team