01
AI engineers
Full-stack engineers who specialize in building products on top of foundation models — prompting, retrieval, agents, evals.
AI Staff Augmentation
Senior AI engineers in your team — in days, not months.
Pre-vetted AI, ML, data, and MLOps engineers who've shipped production AI. From $95/hour, trial sprint included, monthly NTE ceiling so you never get a surprise invoice.
5–10 day staffing · Top 3% of applicants pass · No long-term lock-in
Roles we staff
Six engineering specialties.
02
ML engineers
Training, fine-tuning, deployment of custom models. PyTorch, JAX, TensorFlow when the workload requires.
03
Data engineers
ETL pipelines, data lakes, vector databases. The plumbing that makes AI features reliable.
04
MLOps engineers
CI/CD for models, monitoring, scaling, drift detection. Keeps your AI alive in production.
05
AI security engineers
Prompt injection, data poisoning, output validation, audit logging. Specialists, not generalists.
06
AI product managers
Bridge cool demos and shipped product. Spec-write evals, prioritize ROI, run weekly review rituals.
How we vet
Four gates. Roughly 3% pass.
- Technical screen. Live problem-solving on AI engineering tasks — prompt design, eval harness, RAG architecture. Not generic LeetCode.
- System design. Candidate designs a production AI system end-to-end with one of our principals.
- Reference check. Past clients confirm shipped production work, not just LinkedIn titles.
- Paid trial. Real, scoped work with our team before the engineer faces a customer.
How an engagement starts
Engineer in your team in under two weeks.
01
Brief
Role, stack, time zone, engagement length. 30-minute call to align.
02
Match in 5–10 days
We present 2–3 vetted candidates. You interview them; we don't gate that.
03
Trial sprint
First two weeks are a paid trial. Not a fit? Swap at no charge.
04
Embedded
Engineer joins your Slack, GitHub, Linear. Daily standup, weekly review. NTE ceiling per month.
Hire by specialty
Or scope a project
Recent customer outcomes
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.