Cognite is looking to solve complex business challenges in heavy-asset industries by leveraging AI and data through their offerings like Cognite Atlas AI and Cognite Data Fusion (CDF). The AI Solutions Engineer will bridge the gap between industrial challenges and AI capabilities by leading the rapid prototyping of GenAI-powered solutions.
Requirements
- Proven hands-on experience with GenAI systems (LLMs, embeddings, prompt chaining, etc.).
- Experience building LLM-driven or agent-based applications (e.g., LangChain).
- Solid full-stack development skills, including:
- Frontend: React (or similar frameworks) for building interactive UIs.
- Backend: Python (or similar) for data orchestration and API integration.
- Ability to work across the stack and own the delivery of functional, user-facing solutions.
- Proven ability to write clean, maintainable, and scalable code, following engineering best practices for testing, version control, and review.
Responsibilities
- Build cross-stack prototypes using ATLAS AI, CDF, and open-source AI frameworks to solve real customer challenges.
- Design and implement multi-agent workflows that combine LLMs, tool use, and reasoning over industrial data.
- Collaborate directly with technical teams at customer sites to refine use cases and integrate solutions in real environments.
- Build lightweight but powerful UIs that showcase value to executives and operators alike.
- Orchestrate integration between GenAI components, CDF data models, and existing customer systems using scalable software practices.
- Translate technical solutions into compelling narratives that inspire stakeholders across both internal and external audiences.
Other
- 3+ years of experience in software engineering, AI solution architecture, or similar roles.
- Strong communication and storytelling skills for both technical and executive audiences.
- A maker mindset with bias toward rapid iteration, showing rather than telling, and learning by doing.
- UI/UX sensibilities for building demo interfaces and apps quickly.
- Understanding of industrial data types (e.g., time series, industrial knowledge graphs).
