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Blueprint the AI team you actually need — month by month, at your budget, in your region.

Honest build-vs-augment recommendations: "In London you can realistically hire 2 of the 5 senior AI engineers you need in 12 months. Consider augmenting 3 for the first 6 months." Salary bands across 11 regions. Twenty-four month plan in six minutes.

The roles

Eleven core roles. The wizard picks which ones you need.

  • AI Engineer

    Owns prompts, RAG, evaluation, integration. The first AI hire most teams need.

  • ML Engineer

    Trains and deploys models. Different job from AI Engineer — different loops, different bands.

  • MLOps / Platform

    GPU ops, observability, infrastructure. The role that makes the rest of the team productive.

  • Data Engineer

    Pipelines, retrieval stores, data infra. Quietly the hardest role to skip.

  • AI PM

    AI-native product sense, eval criteria, roadmap. Half the win is what NOT to ship.

  • AI Evaluation / QA

    Evals, red-teaming, test infrastructure. The role that catches model regression before users do.

Regions

Salary bands and time-to-hire that match your reality.

  • US

    Deep talent density, highest comp. SF Bay / NYC remote-friendly post-2024.

  • UK / London

    Deep density, IR35 + tax complexity. Strong applied-scientist pool.

  • Germany / DACH

    Strong ML engineering. Higher loaded multiplier (1.45). Slow but stable hiring.

  • India

    Deep density at junior/mid, growing senior. Time-to-hire often the fastest globally.

  • Singapore / UAE

    Medium density. Premium for senior. Visa friction is real but workable.

  • Remote global

    Widest pool, EOR + tax complexity. 5–9 month time-to-hire is honest.

Methodology

No placement fees. No vendor sponsorships.

Salary bands triangulated across Levels.fyi, Built-In, Payscale, Howdy, Second Talent, LinkedIn Talent Insights, and HeroHunt — refreshed quarterly. The build-vs-augment logic is published in pseudocode so you can pressure-test it.

Read full methodology

FAQ

Common questions about AI team planning.

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

Ship while you hire. Buzzi augments the team you're still building.

Half-day reviews, 4-week pilots, 12-week production engagements. Senior AI engineers, MLOps, and applied scientists ready to own roles you're still recruiting for.

Book a 30-min review