Embed the Top 4% of Applied AI Engineers
We embed engineers from Nasdaq-listed platforms and SaaS unicorns directly into your team — enterprise rigor, certified Agentic SDLC mastery, 4x the output of a traditional engineer from day one.
Finding Applied AI Engineers Is Broken
In-house hiring takes 90+ days and costs a fortune for a US team. Agencies send unvetted contractors with no proof of AI mastery. And the top 4% of Applied AI Engineers don’t respond to job boards — they don’t have to.
Hard To Find AI Talent
96 out of 100 who claim AI mastery are Vibe Coders. We source the 4% who maintain traditional engineering integrity while leveraging modern autonomy.
Agencies Have No Proof
They promise senior AI engineers and send unvetted contractors with no accountability for output. Our engineers come with full technical telemetry and a proven track record.
Your Deadline Won’t Wait
In-house hiring can take 90+ days. Our engineers can deliver their first validated feature in as little as 5 days.
Unmeasurable AI ROI
Most vendors are a black box — no visibility into how your codebase is being touched. Our engineers work inside your environment with
telemetry so you always have the numbers.
How We Do It
We deploy an Applied AI Engineering Pod directly into your environment, assuming full administrative overhead so you can focus on shipping.
The Top 4%
We deploy vetted Applied AI Software Engineers who bypass the usual hiring friction and deliver their first validated feature in as little as 5 days.
Absolute Control
Engineers operate inside the Secure Development Environment. They use your tools, aligning perfectly to your exact sprint cadence.
Elastic Scaling
Scale the embedded teams based on roadmap demands without losing architectural context or dropping your strict 4x sprint velocity.
Zero HR Overhead
We handle global payroll, hardware provisioning, and strict enterprise compliance behind the scenes so you focus on shipping code.
The 4% Vetting Pass Rate
We run a strict 4-stage technical gauntlet to validate true Agentic SDLC mastery. This guarantees your embedded engineers can govern code safely and deliver provable 4x sprint velocity.
Spec-Driven Execution
We test their ability to write strict, machine-readable architectural specifications before they generate any single line of code.
Context Engineering
We test how they navigate and refactor a 50K+ line codebase using AI, proving their ability to strictly manage context boundaries.
System Architecture
We validate their ability to design multi-agent systems, evaluate RAG trade-offs, and safely manage token economics at true scale.
Governance & Safety
Candidates must prove they can implement strict circuit breakers, mitigate prompt injection, and safely resolve AI hallucinations.
Operational and Security Boundaries
How We Embed 4x Applied AI Engineers
Skip the 90+ day hiring cycle. Get a first validated feature in as little as 5 days.
Powering The Enterprise Leaders in Major Verticals
Contact Us
Submit your information to schedule a technical briefing. We will walk you through the 4x Applied AI Engineering model and our under 4-week embedding process.
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FAQ
An Applied AI Software Engineer is a production-grade engineer who uses AI tools — Cursor, Claude Code, and agent frameworks — to architect, build, and ship software at significantly higher velocity than traditional engineers. They don’t just prompt AI; they govern it. They write specifications before code, manage AI context deliberately, and own the quality of every AI-assisted output. GoGloby’s Applied AI Engineers are vetted through a 4-stage funnel — only 4% pass.
A traditional software engineer writes code manually and sequentially. An Applied AI Engineer uses Agentic SDLC — a spec-first, AI-augmented development process — to compress the same work into a fraction of the time. The difference isn’t AI familiarity; it’s AI discipline. Applied AI Engineers manage context boundaries, validate AI output, and prevent hallucinations in production. The output gap is measurable: clients see 4× sprint velocity and 60–70% Agentic AI commit rates within six months.
They join your sprints, work in your environment, and report to your team leads — just like a senior in-house engineer. The difference is in how they work: spec-first before every build, AI-augmented execution throughout, and measurable commit output from day one. The median time to first production commit is 23 days. Your team directs the work. They multiply the output.
You do. Your team sets priorities, runs sprints, and owns direction. The engineer works inside your environment, under your processes, on your roadmap. GoGloby handles the sourcing, vetting, and replacement guarantee — so you get senior-level output without the hiring risk. If something isn’t working, we replace within our guarantee window. No negotiation required.
The median time to first production commit is 23 days. Engineers arrive with a standardised Agentic SDLC workflow already in place — no process ramp-up, no tool configuration guesswork. Day one they’re speccing. Week one they’re building. By sprint three, output velocity is measurable and visible.
A staffing agency screens résumés and places candidates. GoGloby runs a 4-stage technical elimination funnel — Specify, Navigate, Architect, Govern — that only 4% of applicants pass. You don’t receive a pile of CVs; you receive a shortlist of production-proven Applied AI Engineers in 3–5 days. Beyond placement, every engineer arrives with a standardised Agentic SDLC workflow and sprint-level performance visibility built in. GoGloby is an Applied AI Engineering Partner — not a headcount vendor.