Hire Senior AI Engineers

Ship Features, Automate Workflows, Scale Throughput

AI gave your team more code. It did not give you more shipped value. Review backlogs grow, sprint velocity stays flat, and senior engineers spend their week babysitting AI-generated pull requests that nobody trusts.

The hiring market makes that worse. A senior AI engineer in the US averages about $285,000 in total compensation, and Robert Half's 2026 technology salary guide puts mid-level AI and ML engineers at $134,000 to $193,250 in base pay alone. Levels.fyi's AI and ML compensation data shows the same picture at the senior end.

Then there is the wait. RecruitsLab's 2026 AI hiring report puts the average time to fill an AI role at 68 days, up from 42 days in 2023. Specialised roles, agentic architects and MCP integration engineers among them, routinely pass 90 days.

We place senior engineers from nine Asian markets who already ship inside Claude Code, Cursor, and MCP every day. They orchestrate agents, design eval loops, automate CI workflows, and merge production code from the first sprint. Matched in 24 hours, replaced in 14 days if velocity drops.

A US legaltech startup came to us needing an AI engineer for a 50,000-document RAG research assistant. They had searched locally for four months with no offers. They had three vetted profiles in 36 hours, hired within a week, and shipped the assistant to beta six weeks later. Their tech lead said his Filipino engineer "lives inside Claude Code" and cleared the review backlog in the first sprint. More of these in our case studies.

What "AI-Native" Actually Means in 2026

AI tool use is no longer a differentiator. It is the floor. The 2025 Stack Overflow Developer Survey, covering 33,662 respondents, found 84% use or plan to use AI tools, up from 76% a year earlier, and 51% of professional developers use them daily.

The interesting number is the one nobody quotes. In that same survey, only 14.1% of developers use AI agents daily, and 37.9% say they do not plan to use agents at all. Trust explains why: just 3.1% highly trust AI output, while 45.7% actively distrust it.

So the scarcity is not "can this engineer use Copilot". Nearly everyone can. The scarcity is the engineer who can hand work to an agent, verify what comes back, and keep change failure rate flat while throughput climbs. That population is roughly one in seven developers, and it is the population we screen for.

That distinction matters when you write the job description. Screening for "experience with AI tools" selects from 84% of the market and tells you nothing. Screening for shipped agent workflows selects from 14%. Our breakdown of AI-native versus traditional engineers covers the interview signals that separate the two.

What It Costs to Hire an AI Engineer

Rates below are fully loaded monthly cost, not base salary: they include employer contributions, benefits, and our fee. No recruiter percentage, no upfront cost.

Two things drive the range. Market is the first: Vietnam and the Philippines sit at the lower end, Singapore and Hong Kong at the upper. Specialisation is the second: an engineer who has shipped production evals and agent orchestration prices above one who has only built RAG prototypes.

The saving is not a discount on quality. It is the gap between what an engineer costs in San Francisco and what the same engineer costs in Ho Chi Minh City. Full rates by market sit on our pricing page.

Where The AI Talent Actually Is

Not every Asian market is strong at the same thing, and picking on rate alone is how teams end up mismatched. We run owned legal entities in nine markets, so the choice is about fit rather than which country we can reach.

English fluency and timezone overlap matter more than the rate difference between two adjacent markets. An engineer who cannot run a design discussion in English will cost you more in review cycles than you saved on payroll.

What The First 90 Days Look Like

A good AI hire produces something you can point at inside the first month. If the first 90 days are all onboarding, the screen was wrong.

Weeks 1 to 2. Environment access, codebase orientation, and a first merged pull request. Ours merge on day one because the work sample already ran inside a real repository, so the tooling is familiar.

Weeks 3 to 6. A shipped feature behind a flag, with an eval suite covering it. This is the checkpoint that separates engineers who write prompts from engineers who ship systems.

Weeks 7 to 12. Ownership of a workflow end to end, including cost and quality monitoring. By this point the engineer should be reducing your senior team's review load rather than adding to it.

Set the velocity baseline before week one and measure against it. That baseline is also what our 14-day replacement is judged on, so writing it down protects both sides.

Skills and Stack to Screen For

Five areas separate a production AI engineer from someone who has read the documentation. Ask for evidence in each, not familiarity.

Agent orchestration

The engineer should have shipped a multi-step agent workflow into production and be able to explain where it failed. Ask what they did when an agent looped, hallucinated a tool call, or blew a token budget. Fluency shows in the failure stories, not the happy path.

RAG and retrieval

Chunking strategy, embedding choice, and reranking decide whether a RAG system is useful or a demo. Ask how they measured retrieval quality and what they changed after the first evaluation. An engineer who cannot describe their chunking decision has not tuned one.

MCP and tool integration

The Model Context Protocol has become the standard way to expose internal tools to agents. Look for engineers who have written their own MCP server, not only consumed public ones, since that is where authentication and scoping decisions actually live.

Evals and observability

Production AI without evals is untested code shipping daily. Ask what they run in CI to catch prompt regressions, and what dashboards they watch for cost and quality drift. Ragas, DeepEval, LangSmith, and Helicone are the common answers.

Production LLM app engineering

Structured outputs, retries, fallback models, streaming, and cost control are ordinary engineering problems wearing new clothes. A senior engineer treats an LLM call as an unreliable network dependency, because that is what it is.

Hiring for a narrower brief? We staff machine learning engineers and LLM engineers as separate tracks.

Our Hiring Process, Stage By Stage

No take-homes and no multiple choice. Every candidate is verified on live work in a real codebase, and you see the artefacts.

Stage 1: Scope the brief Tell us the use case, the stack, and the sprint velocity baseline you want protected. We write the scorecard from that and send it back for your approval before we screen anyone.

Stage 2: Evidence screen Every shortlisted engineer has shipped production code with Claude Code, Cursor, or equivalent agent tooling in the last 90 days. We show the pull request history, the agent configs, and the eval suites they wrote.

Stage 3: Live Claude Code pull request The candidate scopes a task, orchestrates the agent, writes the evals, and merges the change inside a real repository. Your tech lead can judge the result in 45 minutes.

Stage 4: Your interview Three vetted profiles within 24 hours, interviews inside the week. You talk to engineers who have already passed the work sample, so the conversation is about fit rather than filtering.

Stage 5: Onboard and protect velocity Contracts, payroll, and compliance run through our Employer of Record service. If velocity drops below the baseline set in stage 1, we replace the engineer inside 14 days.

Hiring Locally Against Hiring Through Us

Mistakes That Cost You The Hire

Screening on tool names. "Has used Copilot" describes most of the market. Ask what broke and what they changed.

Skipping the work sample. A resume is now an AI artefact, and an AI-written resume screens beautifully. Only shipped work separates candidates.

Hiring a researcher for a shipping job. Paper credentials do not predict production throughput. If the role is features and workflows, screen for merged pull requests.

Ignoring the eval question. An engineer with no answer on evals will ship prompt changes you cannot measure, and you will find out in production.

Treating time zones as the blocker. Our engineers work your hours. Overlap is a scheduling decision, not a talent constraint. More on how we run distributed teams in our IT staffing services.

Start Hiring

Tell us the use case and the velocity baseline you want protected. Vetted profiles in 24 hours, interviews inside the week, first pull request merged on day one. No upfront fees, 14-day replacement.

For the wider hiring picture, our AI in recruitment statistics tracks how the market is shifting, and the full role list covers every stack we staff.

Hire AI-native engineers matched in 24 hours