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

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• Use AI coding assistants daily as a standard part of delivery, actively, frequently, and with demonstrable impact on productivity and output quality
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• Integrate LLM APIs into applications in production: calling AI provider APIs in live code, managing token limits and latency, and building initial abstraction layers
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• Apply AI across the full software delivery lifecycle: AI-generated tests, AI-assisted debugging, AI-accelerated code review, and prompt engineering for development tasks
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• 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
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• Define and track KPIs to evaluate the effectiveness and ROI of AI-assisted workflows; present AI productivity and quality metrics to project stakeholders
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• Own delivery end-to-end — from design through to production support — in Agile sprint cycles alongside client engineering teams
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• Contribute to shared knowledge bases, reusable components, and internal AI tooling standards that benefit the wider team
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• 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
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Basic Qualifications

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• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related field
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• Commercial software engineering experience in production environments (or equivalent demonstrated through academic projects, internships, or shipped personal projects)
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• Proficiency in at least one primary backend language: Python, Java, or TypeScript
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• 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
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• Basic understanding of web technologies including JavaScript, HTML, and CSS
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• Familiarity with cloud fundamentals (AWS, Azure, or GCP), containers (Docker), and CI/CD pipelines
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• Understanding of Agile delivery fundamentals
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• Experience with databases — SQL or NoSQL
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• Ability to validate, evaluate, and improve AI-generated outputs; understanding of AI limitations and responsible use
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• 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
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