Senior AI engineers embedded in your team, matched in days, not months. Your roadmap, your rituals, our engineers.
A senior AI engineer with production LLM and MLOps experience commands €150–280k in Europe and expects frontier model access, interesting problems, and a team that moves fast. Most product companies outside the top 20 tech firms can't compete on all three. The result: long hiring cycles, expensive mis-hires, or, most commonly, AI initiatives that stall because the right engineer was never in the room. Outstaffing solves this without the risk, the overhead, or the equity dilution of a permanent hire.
The AI talent market is broken for most companies. Senior engineers with production LLM experience are expensive, rare, and not responding to LinkedIn. The teams winning with AI right now aren't the ones with the biggest recruiting budgets, they're the ones who found a smarter way to access the talent. We embed our engineers directly into your team: your Slack, your standups, your sprint cadence. You get the leverage of a specialist without the overhead of a hire.
Engineers with production experience across LLM integration, RAG pipelines, fine-tuning, and MLOps. Matched to your stack, your domain, and the specific problem you're trying to solve, not just keyword-matched.
React, React Native, Node.js, and Python engineers who've shipped in AI-native codebases. They work to your sprint cadence, contribute to architecture decisions, and write the kind of code you'd want to inherit.
A tech lead who runs a sub-team inside your organisation, architecture ownership, code review, mentoring your junior engineers, and bridging the gap between engineering and product. All without the 6-month executive search.
Engineers with verified experience in regulated industries: healthcare (HIPAA, HL7, FHIR), finance (SOC 2, PCI-DSS), defence (security-cleared), and energy (IEC 62443). Rare skills available without the rare search timeline.
For teams pushing the frontier: fine-tuning on proprietary data, RLHF pipeline design, evaluation framework architecture, and custom model development. Ex-research backgrounds with production delivery track records.
Full-time equivalents, part-time specialists, and targeted sprint injections. Monthly rolling contracts. Scale up for a product launch, scale back after, with 30 days' notice and no penalty clauses.
Tools and technologies we use in this practice, chosen for fit, not familiarity.
Consistent across every engagement, adapted to your constraints, not the other way around.
We scope the role together: skills, seniority, timezone, domain knowledge, and team fit. Within 72 hours we present 2–3 matched profiles with a technical summary and relevant project history attached to each. No generic CVs.
The engineer joins your team for a trial sprint, real work, your codebase, your processes. If the fit isn't right technically or culturally, we replace at no cost and no delay. Most engagements pass the trial and move straight to ongoing.
Monthly rolling contract. A dedicated account lead on our side handles performance, feedback, and any escalations, so you get the responsiveness of a direct hire without the HR overhead. Scale up or down with 30 days' notice.
AI outstaffing means placing dedicated AI engineers from 7code — including LLM specialists, ML engineers, AI architects, and data engineers — directly within a client’s team. The engineers work exclusively on the client’s projects, using the client’s tools and processes, under the client’s day-to-day direction, with 7code handling employment, HR, benefits, and technical oversight.
The terms are often used interchangeably, but there is a meaningful distinction. Staff augmentation typically implies adding individuals to supplement a project. AI outstaffing, as 7code practises it, means placing a pre-formed, AI-specialised team that operates with continuity, shared technical standards, and peer review — not a collection of individual contractors working in isolation.
A typical outstaffing team includes one to three senior AI/ML engineers, optionally an AI architect for system design decisions, and a QA engineer for AI evaluation. Team composition is adjusted to the client’s backlog and roadmap. 7code does not outstaff junior engineers — every placed engineer has a minimum of five years of relevant professional experience.
7code can typically present matched engineer profiles within five business days of a signed agreement and completed technical brief. First engineers can be onboarded and operational within two to three weeks. For urgent requirements, a fast-track process is available — contact office@7code.ro with your timeline and 7code will confirm feasibility within 24 hours.
Yes. 7code’s outstaffing service places senior engineers only — defined as a minimum of five years of professional experience, with specific AI/ML domain depth. This policy exists because clients using outstaffing typically need engineers who can operate independently, make sound technical decisions without oversight, and integrate quickly without extended ramp-up periods.
All outstaffing engagements are covered by a comprehensive NDA signed before any technical discussions begin. Engineers placed at client sites sign individual confidentiality agreements as part of their engagement terms. IP developed during the engagement belongs to the client by default. 7code’s standard contract includes explicit IP assignment clauses — clients can also use their own paper.
Onboarding begins with a two-day technical immersion: engineers review the client’s codebase, architecture docs, and toolchain, then meet key stakeholders. By end of week one, they are typically contributing to the active sprint. 7code provides an onboarding checklist and assigns a technical lead as a point of contact for the first 30 days.
Most 7code outstaffing engagements run for six to twenty-four months. The minimum engagement is three months to ensure meaningful ramp-up and delivery. Engagements are governed by rolling monthly terms after the initial period, with a standard 30-day notice clause. Long-term clients often expand team size over time as product scope grows.
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