Hire Prompt Engineers
Eval-driven prompt engineers who treat LLMs as software, not magic. Structured outputs, tool-use, redteaming, regression-tested prompts. From $105/hour, trial sprint included, monthly NTE ceiling.
5–10 day staffing · Top 3% pass · Eval harness from day 1
Sample profiles
Anonymized but accurate. Real engineers on our bench, real rates, real availability.
01
Location
Americas · ET
Background
ex-Anthropic prompt SME, 5y; eval-driven prompt design across reasoning, extraction, and tool-use; published research
Stack
Anthropic · OpenAI · LangSmith · Promptfoo · Pydantic AI · TypeScript
$145/hr
02
Location
Americas · PT
Background
ex-Scale AI, 4y; long-context document workflows; structured-output prompting; LLM-as-judge frameworks
Stack
OpenAI · Anthropic · DSPy · LangSmith · Python · TypeScript
$120/hr
03
Location
EMEA · 6 hr overlap with US ET
Background
ex-Cohere, 4y; multilingual eval; safety + redteaming; tool-use specialist for finance and healthcare workflows
Stack
Cohere · Anthropic · OpenAI · Promptfoo · Python · Postgres
$115/hr
Vetting
01
Live prompt design + iteration on a tricky reasoning or extraction task — including how the candidate measures whether the prompt got better.
02
Candidate designs an eval harness for a production task — golden set, scoring rubric, regression detection — with one of our principals.
03
Past clients confirm shipped LLM features, not just demos.
04
Real, scoped work on our team before the engineer faces a customer.
What prompt engineering means at AISD
Hand-crafting a clever string isn't prompt engineering — it's calligraphy. Real prompt engineering means: a golden test set with 50–500 representative inputs; automatic scoring (LLM-as-judge, exact match, custom rubrics); regression detection on every prompt change; a CI pipeline that blocks merges that degrade quality.
Our prompt engineers also own structured-output design (JSON schema enforcement, retry-on-failure), tool-use prompting, and adversarial testing — the unglamorous work that separates a demo from a production system.
Other engineering roles
Adjacent services
Frequently asked
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.
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.
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.
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.
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.
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.
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.
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.