AI Engineer

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 * Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related field * Commercial software engineering experience in production environments (or equivalent demonstrated through academic projects, internships, or shipped personal projects) * Proficiency in at least one primary backend language: Python, Java, or TypeScript * Demonstrated hands\-on experience using AI tools actively in day\-to\-day engineering work — with practical examples of how AI was used to solve real problems, iterate on outputs, and improve delivery; including direct experience calling LLM APIs in production code with an understanding of token management, latency, and cost tradeoffs * Basic understanding of web technologies including JavaScript, HTML, and CSS * Familiarity with cloud fundamentals (AWS, Azure, or GCP), containers (Docker), and CI/CD pipelines * Understanding of Agile delivery fundamentals * Experience with databases — SQL or NoSQL * Ability to validate, evaluate, and improve AI\-generated outputs; understanding of AI limitations and responsible use * Familiarity with agentic system concepts — awareness of orchestration frameworks (LangChain, LangGraph, or equivalent), RAG pipelines, and how full\-stack applications connect to agent\-based architecture; production experience preferred, conceptual understanding required \#LI\-MP