Prompt + retrieval engineering
Production prompts, golden test sets, hybrid retrieval (semantic + structured), citation enforcement, chunking strategies, freshness controls.
Hire LLM Engineers
LLM engineers — prompts, retrieval, eval, safety.
AISD places LLM engineers who specialize in production-grade language-model systems. Prompt and retrieval engineering, eval harness design, multi-model routing, output safety. Senior, pre-vetted, from $95/hour.
5–10 day staffing · Top 3% pass · Eval-harness specialists
Specialties
Four LLM-engineering specialties.
Eval harness design
Offline eval pipelines, model-graded scoring, golden test set curation, drift monitoring, regression CI integration.
Multi-model routing
Model selection per workload (frontier vs open-weight), prompt caching, structured output validation, fallback strategies, cost caps.
Output safety + guardrails
Prompt-injection defense, PII / PHI redaction, structured-output schema enforcement, jailbreak resistance, audit logging.
When to hire an LLM engineer (vs an AI generalist)
When to specialize.
Hire an LLM specialist when your problem is the model side: quality regressions, cost runaways, prompt injection, or evaluation rigor. Hire an AI generalist when the problem is full-stack — the LLM is one of many systems and you need someone who can integrate end-to-end.
Most production AI products need a mix: 1 LLM specialist + 1–2 AI generalists. We'll size the team to the actual workload.
Frequently asked
Common questions.
How much does it cost to hire an AI developer?
Three pricing paths. AISD staff augmentation (senior, AI-native): $95–175/hour depending on seniority and engagement length. Marketplace contract (Toptal, Turing, Upwork): wide range — $40–200/hour with high variance in quality. Full-time hire (US): total comp typically $200k–$450k for a senior AI engineer. The hidden cost in hires is recruiting (3–6 months) and ramp (2–3 months). Staff augmentation pays off when you need impact in <90 days.
What's AISD's vetting process?
Every AISD engineer passes four gates. Technical screen — live problem-solving on AI engineering tasks (not generic LeetCode). System design — they design a production AI system end-to-end with one of our principals. Reference check — past clients confirm shipped production work. Paid trial sprint — a real, scoped piece of work with our team before the engineer faces a customer. Roughly 3% of applicants pass all four.
How is AISD different from a typical software development agency?
Three differences. First, every AISD engineer is senior — minimum 5 years building production software, with shipped AI features. Second, we publish hourly engagement bands and project ranges so you know roughly what an engagement costs before the first call. Third, we take fewer concurrent projects so a partner stays close to delivery.
How does pricing work — fixed-price, T&M, or retainer?
All three. Fixed-price for AI MVPs and agent builds where scope is well-defined after a discovery sprint. Time-and-materials for staff augmentation, billed monthly with a not-to-exceed ceiling. Retainer for ongoing optimization, eval-harness operations, and managed AI services — flat monthly fee for a defined scope of capacity.
How is success measured on an engagement?
Success metrics are defined in writing during scoping and reviewed monthly. Project engagements measure: feature shipped on date, eval-harness pass rate, target business metric (e.g. 'auto-resolve rate ≥35% on customer-support tickets'). Staff augmentation engagements measure: PR throughput, code-review acceptance, and customer-side satisfaction. We do not measure success in hours billed, lines of code, or generic velocity points.
How long does it take to build a production AI agent?
Working prototype: 2 weeks. Production-grade agent (with eval harness, guardrails, observability, and a runbook): 6–10 weeks. The prototype-to-production gap is where most projects fail — the prototype handles the happy path; production has to handle the long tail.
How long does it take to build an AI MVP?
Most AI MVPs at AISD ship a usable version in 4–8 weeks. Week 1 is a discovery sprint. Weeks 2–6 are the build, with weekly demos and a working version by week 4. Weeks 7–8 harden, document, and hand off.
How is AI consulting different from AI development?
Consulting produces decisions and plans; development produces working software. AISD does both, often in sequence: a consulting engagement scopes the architecture and roadmap, then a build engagement implements it. Consulting alone is right when you're early in the AI journey, evaluating vendors, or auditing existing work. Build alone is right when scope is already clear. Most AISD customers do a 2-week paid discovery sprint first — that's a consulting engagement that produces a fixed-price build proposal.