AI Platform Engineer, Atlas AI

What Cognite is: Relentless to achieve Cognite operates at the forefront of industrial digitalization, building AI , and data solutions that solve the world’s hardest, highest-impact problems. With unmatched industrial heritage and a comprehensive suite of AI capabilities, including low-code AI agents, Cognite accelerates the digital transformation to drive operational improvements. We thrive in challenges.

We challenge assumptions. We execute with speed and ownership. If you view obstacles as signals to step forward - not backwards - you’ll feel right at home here.

Our Moonshot is bold: Unlock $100B in customer value by 2035, and redefine how global industry works. Join us in this venture where AI and data meet ingenuity, and together, we will forge the path to a smarter, more connected industrial future. How you’ll demonstrate Ownership We are seeking an AI Platform Engineer to join the Cognite Atlas AI Product team in Phoenix, AZ, to engineer, build, and operate the production-grade, multi-cloud platform that enables our internal and partner teams to build, deploy, and manage industrial AI agents.

You will be responsible for creating the core services, frameworks, and infrastructure for our "agent builder workbench" and agent runtime, focusing on scalability, reliability, cost-efficiency, and security. Your work will directly impact industrial efficiency and sustainability, which is critical to our mission of powering a high-tech, sustainable, and profitable industrial future. The Impact you bring to Cognite Design, build, and maintain the core Python SDKs and services for the Atlas AI platform.

Create clean abstractions that empower Solution Engineers to easily define and test agents and workflows. Build the core agentic runtime, ensuring it is scalable, meets its SLOs, and can reliably manage the state, orchestration, and execution of industrial agents. Develop a robust, governed, and secure framework for AI agent tool-use.

, API calls, database queries) and that manage the secure execution, monitoring, and access control for those tools. Manage the LLM serving layer, including deploying and optimizing models for low-latency/high-throughput inference. , performance vs.

cost) for a given task. Implement evaluation and observability for all AI services. Create standardized frameworks for systematically evaluating the performance, accuracy, cost, and safety of LLMs and agentic workflows.

Drive the implementation of robust, automated testing strategies for LLM-based systems. Own the full development lifecycle for services in a production SaaS environment. This includes establishing automated code coverage goals, rigorous code reviews, defining SLOs, participating in on-call rotations, and ensuring a fast and effective incident response process.

Work closely with the Lead Architect to translate the technical vision into implemented, production-grade services. Act as a key partner for the Solution Engineers (your internal customers) to understand their needs and abstract common patterns into reusable, robust platform components. Stay up to date on the latest developments in the field, and mentor junior developers.

Required Qualifications

Bachelor's or Master’s degree in Computer Science or a related field, or equivalent practical experience. 5+ years of professional experience in backend software engineering, platform engineering, or MLOps, with a proven track record of architecting and operating complex systems at scale. 2+ years of hands-on experience building applications or platforms on top of AI/ML models or LLMs.

Expert-level proficiency in Python and a strong background in software architecture, robust API design, and building maintainable, well-documented SDKs for other developers.