An engineer reviewing results on two monitors late in the evening in a Bangalore office, headphones around the neck and city lights outside

/Delivery models · Offshore AI

Offshore AI engineers for well-specified work.

Offshore AI engineers work from India, the Philippines or Egypt, many hours ahead of the US. Through partner firms we select and manage, they take on AI work that runs well from a written spec. That includes expanding eval suites, building data pipelines, running batch jobs and clearing integration backlogs. This guide covers which work fits, how overnight eval runs and handoffs work, and how to keep keys and data safe across the gap.

What offshore AI engineering means

The team sits in a distant country with a large time difference. India runs on UTC+5:30 and the Philippines on UTC+8, with no daylight saving in either. Egypt runs on UTC+2, or UTC+3 in summer since it restored daylight saving in 2023.

The model trades shared hours for lower cost and access to large engineering markets. AI work suits it better than you might expect, because so much of it is batch: eval runs, data jobs and model comparisons that take hours anyway. The question is how much of each day needs a live answer from your product team.

Source: IANA Time Zone Database

AI work that fits offshore

Offshore fits AI work with clear inputs, a fixed metric and a clear definition of done. Here’s where we see it work:

  • Eval suite expansion New test cases and scoring rules written to an agreed spec.
  • Data pipeline builds Ingestion, cleaning, chunking and embedding jobs for retrieval.
  • Batch jobs Backfills, re-embedding after a model change and bulk extraction runs.
  • Integration backlogs Connecting models to the APIs and tools your team has already designed.
  • Overnight eval runs and model comparisons, with results written up by your morning.
  • Monitoring dashboards, alerts and runbooks for features already in production.
  • Documentation Prompt registries, data dictionaries and eval reports kept current.

What to keep close

Some AI work needs a conversation more than a spec. Early prototypes, new agent behaviors and anything where product and design are still deciding what good looks like depend on fast back-and-forth. Sent offshore, each question costs a day.

Keep that work nearshore or in-house, and send the follow-on work offshore once the decisions are made. A prototype that passes its evals becomes a backlog of hardening, scaling and monitoring tasks, and that backlog travels well.

Overnight eval runs and written handoffs

The time gap can work for you. Your team changes a prompt or a retrieval setting at the end of its day. The offshore team runs the full eval suite overnight, digs into the failures and has a written report ready by your morning.

That only works if the handoff is good on both sides. Every report should be readable by someone who wasn’t there.

  • Which prompt, model, dataset and code version was tested, with links.
  • Scores against the last accepted run, by metric, with cost and latency.
  • The worst failing cases, quoted in full, with a short note on each.
  • What the team thinks caused the change, and what it would try next.
  • Open questions at the top, each with a named owner on your side.

Writing AI tickets that survive the time gap

Every unclear ticket costs a day. A good offshore AI ticket names the data, the metric and the threshold, not only the feature. If an engineer has to guess which eval set counts, they’ll either guess wrong or wait for your morning.

Include the eval set and its version, the score that counts as done, a cost or latency budget and what’s out of scope. If a decision is still open, say so and name who decides.

India, the Philippines or Egypt for AI work

India has the largest engineering market of the three and a wide range of data and ML skills. At UTC+5:30 it’s nine and a half to ten and a half hours ahead of US Eastern time, so a shared window needs someone to shift their day.

The Philippines, on UTC+8, is twelve or thirteen hours ahead of the US East Coast. Its evening is your morning, which suits teams that agree a shifted schedule. Egypt sits closest, about seven hours ahead of US Eastern for most of the year, so a late-starting team can share part of your morning.

We choose per team: the skills first, then the overlap your leads need, then cost.

Offshore vs onshore and nearshore for AI work

A general comparison for AI engineering. Use it to frame questions for any partner.

OnshoreNearshore (Mexico, Colombia)Offshore (India, Philippines, Egypt)
Relative costHighestMiddleLowest
Overlap with US hoursFullMost or all of the dayShort, or none without shifted hours
Eval feedback loopSame daySame dayOvernight runs, written reports
Best fitRoles that need US residency or on-site accessPrototypes, RAG and agent design, ambiguous requirementsEval suite expansion, data pipelines, batch jobs, integration backlogs
Onshore vs nearshore vs offshore The full comparison across every service line. Offshore software development Offshore teams for general product and platform work.

Keys, data and security across borders

Distance raises the stakes on access. An offshore engineer with a vendor’s API key, a copy of your data on a laptop and no audit trail is a risk you can’t see. Set the rules before the first ticket.

NIST’s Generative AI Profile (NIST AI 600-1, July 2024) lists risks specific to generative AI, including data privacy and information security. The OWASP Top 10 for LLM Applications adds prompt injection and unbounded consumption.

Sources: NIST AI 600-1, Generative AI Profile (PDF) OWASP Top 10 for LLM Applications

  • 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, with spending limits on model APIs.
  • Secrets in your secrets manager, never in code, prompts or notebooks.
  • Production data stays in your environment; engineers work on masked or sampled copies where they can.
  • No customer data in third-party tools without your written approval.
  • Code, prompts, eval sets and model artifacts assigned to your company in the contract.

What drives the cost

We don’t publish rates, but the drivers are the same for any offshore AI team. Seniority comes first, then the mix of AI, ML and data roles, then team size and the overlap you need.

Two costs hide outside the rate. One is your leads’ time spent writing specs and reading reports. The other is model API and compute spend, which an unwatched batch job can run up overnight. Add both before you compare offshore with nearshore.

Blending offshore with nearshore

Many AI teams get the best result from a blend. Prototypes and product decisions sit nearshore, in your hours. Eval expansion, pipelines and batch work run offshore, with the nearshore leads writing specs and reviewing the output.

Teams are delivered through partner firms OTRO selects and manages, under one contract and one point of contact. You don’t have to blend on day one: start with one model and add the other when the work calls for it.

AI integration services Help deciding what to build and putting models to work. Offshore data annotation Human labeling queues that run overnight on written guidelines. Samacare: AI-Powered Healthcare Platform Optimization LLM-based data extraction in a healthcare workflow.

Frequently asked questions

Which AI work should I send to offshore AI engineers?

Send work with clear inputs, a fixed metric and a clear definition of done. Eval suite expansion, data pipeline builds, batch jobs, integration backlogs and overnight eval runs all fit. Keep early prototypes and agent design close to your product team, then send the hardening and scaling work offshore once the main decisions are made.

Is Mexico offshore or nearshore for AI work?

For US companies, Mexico is nearshore. Most of the country stays on UTC−6 all year, within two hours of every mainland US time zone, so the team shares most of your working day. Offshore means distant countries such as India, the Philippines and Egypt, where the day barely overlaps with yours unless someone shifts their hours.

How do overnight eval runs work with an offshore team?

Your team makes a change at the end of its day and tags the version. The offshore team runs the full eval suite, reviews the failures and writes a report with scores, cost, latency and the worst cases quoted in full. You read it in the morning and decide the next change, so the loop keeps moving around the clock.

How much does an offshore AI team cost?

Offshore is the lowest-cost of the three delivery models, below nearshore and onshore. The exact figure depends on seniority, the role mix and how much overlap you need, so we don’t publish rates. Add the hidden costs before comparing: your leads’ review time and the model API spend that batch jobs generate.

Can offshore engineers use our production data?

Only under rules you set first. Keep production data in your own environment, give engineers masked or sampled copies where the task allows, and log who accessed what. Check what your customer contracts and privacy laws say about processing outside the US, and get your counsel’s view before any personal data leaves your systems.

Who owns the prompts, eval sets and models?

You do. 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, under your API keys, so you can remove access at any time. Ask your counsel to confirm the assignment covers the partner firm’s engineers too, not only OTRO.

Plan your offshore or blended AI team

Tell us what you’re building, the AI work you want to move and the hours your leads keep. We’ll come back with a written plan: which work goes where, the roles and the overlap window we’d set.

Plan your team