Role Description

We are building the next generation of AI-native engineering talent engineers who use AI as a core part of how they work, not as an add-on. As an AI Engineer (Software), you will design, build, and ship production-grade software across the full stack, using AI-assisted tooling as standard daily practice alongside your core engineering skills.

You will work on real client programs across industries, building production-grade software that connects to and supports agentic AI systems understanding how your full-stack work integrates with agent architecture, LLM APIs, and enterprise AI pipelines. This is not a stepping-stone role: it is a core engineering function in the most in-demand part of the market, with a direct pathway to the Forward Deployed Engineer program for those who develop agentic depth.

We offer what no single product company can: breadth across every industry, every enterprise technology stack, and every level of organizational complexity combined with vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google engineering teams, structured AI certification pathways, and a clear development track toward agentic and forward-deployed engineering.

Key Responsibilities

• Use AI coding assistants daily as a standard part of delivery, actively, frequently, and with demonstrable impact on productivity and output quality

• Integrate LLM APIs into applications in production: calling AI provider APIs in live code, managing token limits and latency, and building initial abstraction layers

• Apply AI across the full software delivery lifecycle: AI-generated tests, AI-assisted debugging, AI-accelerated code review, and prompt engineering for development tasks

• Own the quality of AI-generated outputs in your delivery scope, exercise engineering judgment about reliability, limitations, and failure modes; know when AI output is production-ready and when it is not

• Define and track KPIs to evaluate the effectiveness and ROI of AI-assisted workflows; present AI productivity and quality metrics to project stakeholders

• Own delivery end-to-end from design through to production support in Agile sprint cycles alongside client engineering teams

• Contribute to shared knowledge bases, reusable components, and internal AI tooling standards that benefit the wider team

• Build and integrate the application layers, APIs, and interfaces that connect full-stack systems to agentic backends understanding data flows, context handoffs, and integration points between your code and AI pipelines