TL;DR: Hire senior AI-native engineers in Southeast Asia for $3,500–$8,500/mo. 60–75% lower loaded cost than US hires. Engineers who live in Claude Code, Cursor, and MCP every day. First PR merged on day one.
Ship Features, Automate Workflows, Scale Throughput
AI gave your team more code. Not more shipped value. Review backlogs are growing. Sprint velocity is flat. Senior engineers are drowning in AI-generated PRs that need babysitting. Meanwhile, a US senior AI engineer costs $290K all-in and takes 4.6 months to fill.
We fix that. We place senior engineers from Southeast Asia who already live in Claude Code, Cursor, and MCP in production every day. They orchestrate agents, design eval loops, automate CI workflows, and ship production code from day one. Matched in 24 hours. Replaced in 14 days if sprint velocity drops below your baseline.
We worked with a US legaltech startup that needed an AI engineer to build a 50K-document RAG-powered research assistant. They had been searching locally for four months with no offers. Through Second Talent they had three vetted profiles in 36 hours, hired in a week, and shipped the assistant into beta six weeks later. The team lead said his Filipino engineer "lives inside Claude Code" and cleared the review backlog within the first sprint.
| Factor |
Hiring Locally (US) |
Second Talent |
| Time to first shortlist |
12–24 weeks |
24 hours |
| First PR merged |
90–120 days |
Day one |
| Loaded monthly cost |
$18,000–$24,000 |
$3,500–$8,500 |
| Recruiter fee |
20–25% of first-year salary |
$0 upfront |
| Vetting |
Done by you |
Live Claude Code pairing, top 1% |
| Replacement guarantee |
None |
14-day velocity-backed |
What "AI-Native" Actually Means in 2026
Tool adoption is the baseline. Every developer has Copilot now. The real bar is whether engineers can orchestrate agents responsibly, design eval loops, integrate MCP servers, and keep change failure rate flat while velocity climbs.
A senior AI-native engineer should be fluent in Python and TypeScript, ship daily inside Claude Code or Cursor, and have production experience with at least one orchestration framework. They should understand RAG patterns, embeddings, vector databases, prompt engineering under structured outputs, and automated eval pipelines.
| Specialization |
Stack They Live In |
Real Output |
| RAG + retrieval |
LangChain, LlamaIndex, Pinecone, Qdrant |
Internal copilots, support agents, research assistants |
| Agent orchestration |
Claude Code, LangGraph, CrewAI, AutoGen |
Multi-step reasoning, sales agents, ops automation |
| MCP integration |
Model Context Protocol servers, custom tools |
Internal tool chains, agent/tool handoffs |
| LLM app engineering |
OpenAI / Anthropic APIs, structured outputs |
Production features in existing SaaS |
| Eval + observability |
Ragas, DeepEval, LangSmith, Helicone |
Prompt regression in CI, cost + quality dashboards |
How We Vet AI-Native Engineers
No take-homes. No multiple-choice. We verify on live work. Every candidate ships a Claude Code PR inside a real codebase: scope the task, orchestrate the agent, write the evals, merge the change. Your tech lead can judge the result in 45 minutes.
Every engineer we shortlist has shipped production code using Claude Code, Cursor, or equivalent agent tooling in the last 90 days. We show you the PR history, the agent configs, and the eval suites they wrote. Verifiable, not claimed.
Hiring Process
Tell us the use case, the stack you live in, and the sprint velocity baseline you want to protect. Vetted profiles in 24 hours. Interviews inside the week. First PR merged by day one. No upfront fees. 14-day velocity-backed replacement.
Contracts, payroll, and compliance run through our Employer of Record service. You focus on shipping.
Hire AI-native engineers matched in 24 hours →
