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Plane das AI-Team, das du wirklich brauchst — Monat für Monat, in deinem Budget, in deiner Region.

Ehrliche Build-vs-Augment-Empfehlungen: „In London kannst du in 12 Monaten realistisch 2 der 5 Senior-AI-Engineers einstellen, die du brauchst. Erwäge, 3 für die ersten 6 Monate zu augmentieren.“ Gehaltsbänder über 11 Regionen. 24-Monats-Plan in sechs Minuten.

Die Rollen

Elf Kernrollen. Der Wizard wählt, welche du brauchst.

  • AI Engineer

    Verantwortlich für Prompts, RAG, Evaluation, Integration. Die erste AI-Einstellung, die die meisten Teams brauchen.

  • ML Engineer

    Trainiert und deployt Modelle. Ein anderer Job als AI Engineer — andere Loops, andere Bänder.

  • MLOps / Platform

    GPU-Ops, Observability, Infrastruktur. Die Rolle, die den Rest des Teams produktiv macht.

  • Data Engineer

    Pipelines, Retrieval-Stores, Daten-Infra. Im Stillen die schwierigste Rolle zum Auslassen.

  • AI PM

    AI-natives Produktverständnis, Eval-Kriterien, Roadmap. Die Hälfte des Erfolgs ist, was NICHT ausgeliefert wird.

  • AI Evaluation / QA

    Evals, Red-Teaming, Test-Infrastruktur. Die Rolle, die Modell-Regressionen vor den Nutzern erkennt.

Regionen

Gehaltsbänder und Time-to-Hire, die zu deiner Realität passen.

  • USA

    Tiefe Talentdichte, höchste Comp. SF Bay / NYC remote-friendly nach 2024.

  • UK / London

    Tiefe Dichte, IR35- + Steuerkomplexität. Starker Pool an Applied Scientists.

  • Deutschland / DACH

    Starke ML-Engineering-Szene. Höherer geladener Multiplikator (1,45). Langsame, aber stabile Einstellungen.

  • Indien

    Tiefe Dichte bei Junior/Mid, Senior wachsend. Time-to-Hire oft die schnellste weltweit.

  • Singapur / VAE

    Mittlere Dichte. Premium für Senior. Visa-Reibung ist real, aber machbar.

  • Global Remote

    Größter Pool, EOR- + Steuerkomplexität. 5–9 Monate Time-to-Hire, das ist ehrlich.

Methodik

Keine Vermittlungsgebühren. Keine Anbieter-Sponsorings.

Gehaltsbänder triangulieren über Levels.fyi, Built-In, Payscale, Howdy, Second Talent, LinkedIn Talent Insights und HeroHunt — quartalsweise aktualisiert. Die Build-vs-Augment-Logik ist als Pseudocode veröffentlicht, damit du sie unter Druck setzen kannst.

Vollständige Methodik lesen

FAQ

Häufige Fragen zur AI-Teamplanung.

What does the Buzzi.ai Team Blueprint tool do?

It produces a month-by-month AI team plan tailored to your project type, scale, region, timeline, and budget — with build-vs-augment recommendations, salary-banded budget envelope, and a JD-template pack you can paste into your ATS.

Why does the tool separate AI Engineer from ML Engineer?

Different jobs. AI Engineer owns prompts, RAG, evaluation, integration. ML Engineer trains and deploys models. Hiring loops, comp bands, and skill profiles diverge — collapsing them produces wrong plans.

Where do the salary bands come from?

Triangulated across Levels.fyi, Built-In, Payscale, Howdy (LATAM/India), Second Talent (APAC/MENA), LinkedIn Talent Insights, and HeroHunt time-to-hire data — refreshed quarterly.

What does "loaded multiplier" mean?

Cost-to-employer beyond base. Includes payroll taxes, benefits, equipment, and (region-specific) housing allowance, super, IR35 carve-outs, EOR fees. Defaults: US 1.30, UK 1.30, Germany 1.45, Singapore 1.30, Australia 1.25, India 1.20, UAE 1.20, Canada 1.25.

How does the build-vs-augment logic work?

Tight timeline + medium-density region + senior-or-above seniority → augment recommended for the bridge months. Build posture override respects your preference but still surfaces the gap.

When should I hire a junior vs a senior?

Pilot scale prefers junior-to-mid with a senior anchor. Mid and enterprise production scale need senior + staff layered, with leadership earlier (month 3-6).

When does augmentation win over hiring?

When time-to-hire exceeds half your timeline, when seniority is hard to source in-region, or when you need a deep skill for less than 6 months.

What's the typical time-to-hire in different regions?

Deep markets (US, UK, Germany, India): 2–4 months. Medium markets (UAE, Singapore, Australia, Canada, France, Netherlands): 3–6. Global-remote: 5–9 with EOR / tax complexity.

Are the JD templates authentic, or AI-generated boilerplate?

JD packs use a Buzzi-curated template per role with placeholders for your stack and AI-generated "About our AI work" blocks personalised to your inputs. Always reviewed by counsel before posting.

Do you cover regional labor laws?

Yes for the major contours: IR35 (UK), super (Australia), EOR / WIF (global), 13th-month pay (Germany / EU), DPDPA (India). The methodology page links to the authoritative sources.

How do remote-hiring tax complexities work?

Loaded multipliers reflect typical EOR fees (10–15%). For >5 remote hires, build a permanent establishment analysis — the methodology page includes the framework.

How does equity differ by stage?

Seed: 0.10–0.50% senior IC. Series A: 0.05–0.20%. Series B+: smaller, RSU-style at later stages. v1 reports band only — refined modelling planned.

What visa considerations apply?

H-1B (US), Skilled Worker (UK), Blue Card (EU), Green List (UAE), Tech.Pass (Singapore). Each adds 1–3 months to time-to-hire and constraints on seniority swaps.

How often are salary bands updated?

Quarterly refresh from primary sources, with hot-region overrides as labour markets move. Last verified date is shown in admin per band.

How confident are you in the salary data?

Source confidence scored 0.0–1.0 per band. Deep markets (US / UK / India) score 0.85+. Medium markets 0.65–0.80. Global remote 0.55–0.70. The methodology page calls out where uncertainty is highest.

Buzzi-Services

Liefere, während du einstellst. Buzzi augmentiert das Team, das du noch aufbaust.

Halbtagesreviews, 4-Wochen-Piloten, 12-Wochen-Produktions-Engagements. Senior AI Engineers, MLOps und Applied Scientists, bereit, Rollen zu übernehmen, die du noch rekrutierst.

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