As a hands on AI Engineer, you will be at the heart of designing and building the components that make up advanced AI systems powering the modern enterprise. This is a deeply technical, hands-on engineering — you will spend the majority of your time in the detailed design, development, integration, and testing of AI system components across classical machine learning, generative AI, and agentic systems, delivering these within active client engagements.
You will take detailed architecture and design specifications and translate them into working, production-quality software components. This means writing clean, well-structured code, making low-level design decisions within your assigned scope, and ensuring your components integrate reliably within the broader AI system. You will build and wire together the constituent parts of AI agent systems — including individual agent logic, tool integrations, skills, and memory components — and contribute to the development and integration of foundation and classical ML models into end-to-end pipelines.
A hands-on curiosity for the open source ecosystem is essential in this . You will continuously evaluate, learn, and adopt relevant open source libraries and frameworks — such as those spanning agent orchestration, vector storage, model serving, and ML pipelines — selecting and applying the right ones for the problem at hand. Equally, you will configure, integrate, and operationalize third-party AI technologies and platform services, understanding their capabilities and constraints deeply enough to make them work reliably within the context of a larger enterprise system. You will engineer components with enterprise-grade qualities in mind, ensuring your work meets defined requirements across security, observability, governance, performance, and scalability. You will write and maintain the technical artifacts that accompany your engineering work — including low-level design documents, component specifications, and integration contracts — ensuring your work is well-documented, testable, and handoff-ready. You will operate as a practitioner within cross-functional delivery teams alongside data engineers, ML engineers, and application developers, taking direction from lead and principal architects while contributing meaningfully to technical problem-solving and design discussions within your domain. This is an opportunity to build deep, hands-on expertise across the AI engineering stack, develop strong software engineering fundamentals applied to cutting-edge AI systems, and grow toward a lead engineer or architect over time.
