AI Native Software Engineering

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 Basic Qualifications - 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