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.

About the Role:

We are looking for a Senior Engineer to lead the production systems for our Context Services team. You will serve as the bridge between our Applied Scientists/AI Engineers and the central Platform Engineering team. In this role, you will focus on turning complex analytical solutions—ranging from traditional ML/DL models to modern LLM agents—into highly reliable, production-grade software services.

The Impact you bring to Cognite

Key Responsibilities:

• Architecture & Scale: Design robust system architectures and develop low-latency APIs/services capable of supporting thousands of concurrent users with sub-second response times.
• Cross-Functional Problem Solving: Collaborate closely with product, architecture, and AI teams to translate complex industrial data problems into scalable solutions.
• AI & LLM Orchestration: Architect frameworks for model serving, vector databases, and agentic workflows (LLM agents), ensuring reliable state management and prompt tracking.
• Platform Integration: Act as the technical liaison with the central Platform team, leveraging core infrastructure to build reliable AI pipelines and workflows.
• Innovation: Drive technical innovation through research, prototyping, and potential contributions to open-source initiatives.

Qualifications & Skills:

• Experience: 6–10 years of industry experience in Software Engineering, Backend Engineering, or Systems Engineering, with exposure to AI, ML, or LLM-based production systems.
• Programming: Strong proficiency in Python and experience building high-concurrency applications, asynchronous programming, or REST/gRPC APIs.
• Systems: Solid understanding of containerization (Docker) and modern deployment environments (such as Kubernetes or cloud platforms).
• AI Knowledge: Familiarity with the broader AI lifecycle—including traditional model registries, vector databases, or LLM evaluation frameworks—is a major plus, but a strong ability to learn new paradigms is essential.