TL;DR: Hire LLM app engineers in Southeast Asia for $3,000–$7,500/mo. 60–75% lower loaded cost than US hires. Engineers who ship production features with OpenAI, Claude, and structured outputs every day. First PR merged on day one.
Ship LLM Features. Not Demos. Shipped Code.
Every CTO has the same complaint. The team built a demo in a weekend. Getting it to production took four months. By then the requirements changed, the prompts degraded, and nobody wrote evals.
That is the gap our LLM app engineers close. They ship features inside your existing product, not new standalone AI apps. They wire OpenAI and Anthropic into your backend with structured outputs, streaming, function calling, and the Assistants API. They write the eval regressions that catch prompt drift in CI. They understand cost per request, latency budgets, and when to cache.
Senior US engineers with this depth run $135,000–$170,000 per year and are nearly impossible to poach. Our Southeast Asian seniors start at $3,000/month, match in 24 hours, and open their first PR inside your codebase on day one.
We worked with a US legal services company that needed an engineer to build a custom GPT for internal case-law research. Through Second Talent they hired in five days. The feature shipped a month later and saved each researcher an estimated 12 hours per week. The CTO said he should have hired remote six months earlier.
| Factor | Hiring Locally (US) | Second Talent |
|---|---|---|
| Time to first shortlist | 8–14 weeks | 24 hours |
| First PR merged | 60–90 days | Day one |
| Loaded monthly cost | $13,000–$19,000 | $3,000–$7,500 |
| Recruiter fee | 15–20% of salary | $0 upfront |
| Vetting | Done by you | Live LLM build, top 1% |
| Replacement guarantee | None | 14-day velocity-backed |
What Senior LLM App Engineers Actually Ship
A senior LLM app engineer should be fluent in the OpenAI API (GPT-4 / 4o / 5, function calling, structured outputs, streaming, vision, Whisper, embeddings, Assistants API) and the Anthropic API (Claude family, tool use, prompt caching, agent SDK). They should ship daily inside Claude Code or Cursor.
Prompt engineering is the floor. The real bar is eval discipline, cost / latency tradeoffs, and production hardening. They pick structured outputs over free text when the data matters. They cache and stream when the latency matters. They write prompt regression tests in CI so nothing silently degrades.
| Specialisation | Stack They Live In | Real Output |
|---|---|---|
| Product features | OpenAI, Anthropic, structured outputs | In-app copilots, smart search, auto-fill, summarisation |
| Custom GPTs + Assistants | Assistants API, function calling, retrieval | Internal research, deal desk, compliance review |
| RAG features | LangChain, Pinecone, pgvector | Knowledge-base chat inside existing SaaS |
| Voice + multimodal | Whisper, GPT-4o, vision, TTS | Meeting notes, voice agents, document OCR |
| Evals + observability | Ragas, DeepEval, LangSmith, Helicone | Prompt regression, cost dashboards, A/B tests |
How We Vet LLM App Engineers
No take-homes. No multiple-choice. We verify on live work. Every candidate ships an LLM feature inside a real codebase during the final interview: design the prompt, pick structured outputs or free text, add streaming, write the eval regression, merge the PR. Your tech lead can judge the output in 45 minutes.
Every engineer we shortlist has shipped production LLM code in the last 90 days. We show you the PRs, the eval suites, and the cost dashboards they have set up.
Only the top 1% pass.
Hiring Process
Tell us the feature you want to ship and the stack you live in. Vetted profiles in 24 hours. Interviews inside the week. First PR merged on day one.
No upfront fees. No recruiter commission. 14-day velocity-backed replacement. Contracts, payroll, and compliance via our Employer of Record service.
