Senior/Staff AI Engineer — Agentic Development (Next.js/Node.js)

Department: G2i Eng Team

Location: Miami, Calgary, Mississauga, Vancouver, Boston, Montreal, Denver, Oklahoma City, Nashville, Indianapolis, Chicago, Huston, Seattle, Chesapeake, Atlanta, Ottawa, Toronto, Austin, Norfolk, Dallas, Virginia Beach, San Antonio, Philadelphia, Arlington, Richmond, Savannah, Columbus, Las Vegas, Winnipeg, Boise, Fairfax, Fort Worth, Jacksonville, Edmonton, Reno, Washington, D.C.

Employment Type: Contract

Contract | 40–80 hrs/week

Roles open on a rolling basis - apply to join the talent bench and we’ll reach out when one matches. Expect 40+ hrs once a project starts; timing depends on availability, but we move people in at the earliest genuine opportunity.

We're looking for senior-to-staff+ software engineers to build in a fast-moving, high-ambiguity environment with tight deadlines and demanding KPIs. This is hands-on engineering work for people who know how to move quickly and operate with minimal direction.

What You'll Be Doing

You'll be building agentic applications and tooling at the frontier of LLM-powered development. This means working directly with modern coding agents and LLM infrastructure, shipping fast in an environment where requirements shift, and the bar is high. You'll need to make strong judgment calls under pressure and deliver without hand-holding.

Tech Stack

The core stack is Next.js/Node.js with TypeScript throughout — typed end-to-end, no exceptions. You should be deeply fluent in this ecosystem rather than just familiar with it.

Who You Are

You're a minimum senior-level engineer, though staff+ level is ideal. You have hands-on agentic development experience, including practical knowledge of LLMs and familiarity with the tooling ecosystem for coding agents. You thrive in highly ambiguous environments, work well under tight deadlines, and are comfortable being measured against very high KPIs. You move fast without sacrificing quality.

Nice to Have

RAG application development experience is a plus. A background in reinforcement learning (RL) is also valuable.

Engagement Details

• Hours: 40 minimum, up to 80 per week

• Environment: High-ambiguity, deadline-driven, high-KPI