Overview
A global out‑of‑home (OOH) advertising company engaged TeamStation AI to accelerate development of AI‑assisted media‑planning software. Under a staff‑augmentation model, two senior Full‑Stack Engineers based in Latin America were embedded into the client’s teams for 12 months, operating in the client’s tools and under the client’s technical leadership. TeamStation AI supplied the talent pipeline, evidence‑backed onboarding, and security governance needed to contribute quickly and safely.
Context & Stakes (CTO Lens)
- AI feature velocity: Ship LLM‑integrated planning capabilities without destabilizing core systems.
- Talent scarcity: Senior full-stack engineers with Python, React/TypeScript, AWS, and prompt‑engineering experience are scarce and slow to hire.
- Operational risk: Remote access, device posture, and data protections must meet enterprise standards from day one.
- Cost discipline: Expand capacity without committing to permanent headcount or lengthy recruiting cycles.
Roles & Scope (SOW‑001)
Full‑Stack Engineer (x2)
- Stack target: Python, React/TypeScript, SQL Server/Postgres, AWS, CI/CD; experience integrating LLM services and responsible prompt engineering.
- Responsibilities: Participate in stand‑ups and planning; design and deliver features; write unit/integration tests; contribute to system design; document code and decisions; collaborate cross‑functionally with data science, product, and design.
Selection & Onboarding
Selection rigor
- Structured work‑sample tests aligned to the stack (Python concurrency & API design, React state for real‑time dashboards, CI/CD strategy).
- System‑design and security scenarios; prompt‑engineering exercises for LLM usage; communication and leadership signals captured with rubric-based scoring.
Talent Integration & Acceleration (Day‑0 → Day‑90)
- T‑14 to T‑1: Device and software baselining; MFA/SSO and least‑privilege access; environment readiness; documentation uploaded to a personal portal.
- Week 1: Team intros, ceremonies, and a “first ticket” to land a small, production‑adjacent contribution.
- 30‑60‑90 plan: From component work to feature ownership with explicit milestones; progress tracked in the client’s tools.
This structure reduces friction, ensures security compliance, and accelerates time‑to‑productivity—all evidenced through an onboarding dossier.
What Changed
- More bandwidth for AI features without compromising stability.
- Faster iteration thanks to time‑zone overlap, seniority fit, and an evidence‑backed onboarding path.
- Lower operational risk with managed devices, MFA/SSO, least‑privilege access, and documented handoffs.
- Cleaner documentation (PR notes, ADRs where needed) that ages with the codebase.
What CTOs Can Learn From This Engagement
- Staff augmentation can be engineered, not ad hoc. Treat talent selection, onboarding, and security as an integrated system with artifacts you can audit.
- AI literacy matters at the full‑stack layer. Teams ship safer when engineers understand prompts, model limits, and evaluation strategies—not just SDK calls.
- Documentation is part of the deliverable. Lightweight ADRs and PR notes preserve decision context and reduce regression risk.
- Feature flags are your best brake and accelerator. They make faster releases compatible with lower risk.
- Time‑zone alignment is not a perk; it’s throughput. Same‑day feedback compounds over sprints.