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.
As a Senior AI Agent Engineer, you will be the primary owner and architect for building, enhancing and maintaining a packaged , customer ready solution of agentic workflows geared to integrated supply chain pumping the heart of industrial digital transformation.
You are not just building models, you are building agents capable of traversing the Cognite Data Fusion (CDF) Knowledge Graph to automate complex supply chain decisions.
You will be part of the CoE team working closely with our global customers across industries, and integrating and scaling the packaged agents in their daily operations. You will lead discovery workshops, and reimagine and define new ways of working with agents as co- workers. You will incorporate new learning, usable across customers, in the packaged agent offering.
Use Cognite Flows to enable workflows for visibility, exception surfacing, root cause analysis and recommendations. Also work with partners to enable end to end workflows for adaptive planning and action.
You will work with a data model that integrates Maintenance Execution (Work Orders), Production Planning (Batches/Recipes), Inventory & Inbound/Outbound Logistics, (Shipments/Deliveries), suppliers & customers and external market & risk data. The agents will need to "reason" across these domains.
The role will require deep collaboration with sales, product , value delivery and partners.
- with product, to ensure alignment with product standards and quality. Make it product-ready
- with Value Delivery, to support during initial rollouts or pilot deployments, providing technical depth where needed and helping troubleshoot agent behaviour in context and develop a feedback process, in order to improve use case / agent readiness and alignment with customer expectations
- with Field Engineering, in scoping and qualifying AI agent use cases — helping assess technical feasibility, data readiness, and fit between customer environments and packaged agent assumptions. Also, gathering structured feedback from pre-sales interactions on common customer objections, technical concerns, and unmet requirements — and feeding these signals back into agent design and packaging priorities
- with a partner ecosystem, to develop/ enhance the agentic solutions for pilot customers, and close loop feedback from partners feeding into agent design and enhancements.
- Supply Chain Agents: Develop modular, multi-agent systems using ATLAS AI that leverage tool-use, skills, memory, and multi-step reasoning (Chain-of-Thought) to interact with the CDF Knowledge Graph for solving supply chain problems. This should be product ready and handed to the product and delivery organisation.
- Knowledge Graph Orchestration: Design and build precise, reliable tools that can query and traverse the supply chain knowledge graph — handling complex relationships, multi-hop lookups, and returning accurate structured data. The supply chain agents should be able to reason over it.
- Domain-Specific Reasoning: Craft advanced prompt chains and evaluation frameworks to ensure agents provide accurate, operationally safe recommendations for industrial use cases.
- Rapid development and deployment: Operates with high autonomy and minimal guidance to build functional prototypes directly on customer data or synthetic data, test with customers and SMEs and support integration with customers working along with a value-delivery team.
- 6-9 Years in AI/ML Engineering: Proven track record of delivering production-grade AI features. Hands-on experience with LLMs, embeddings, prompt engineering, and related techniques.
- Advanced Agent Frameworks: Experience building LLM-driven or agentic systems (e.g., using LangChain or similar frameworks).
- Knowledge Graph Proficiency: Ability to query and navigate complex relational and graph data models. You should be comfortable thinking in "nodes and edges".
- You're a strong full-stack engineer: You can work across the stack and own delivery of functional, user-facing solutions: Frontend: React (or similar) + TypeScript, Backend: Python, Kotlin, or similar for orchestration and API integration.
- Industrial and Supply Chain Data Literacy: Deep understanding of how to work with "imperfect" industrial data and cross functional supply chain data, including time series and sparse metadata.
- Autonomous Maker Mindset: A "show, don't tell" approach. Bias towards rapid iteration. You can take a schema (like the Integrated Supply Chain model) and independently identify how to build an agent that provides value from it.
- Familiarity with AI-powered IDEs : Experience using coding assistants (Cursor, Claude Code, Copilot, or similar) and comfortable using LLMs to accelerate development, debugging, and documentation tasks
- You communicate clearly: Strong storytelling skills for both technical and executive audiences.
- You write quality code: Clean, maintainable, and scalable—with solid practices around testing, version control, and code review.
- Supply Chain Domain Expertise: Prior experience with ERP (SAP) data structures—specifically MM (Materials Management), PP (Production Planning), and PM (Plant Maintenance). Experience with Supply chain platform solutions like o9, Kinaxis, OMP
- ISA-95/ISA-88/ SCOR Knowledge: Understanding the standards for automated manufacturing and batch control, SCOR DS frameworks and other supply chain frameworks.
- Cognite Data Fusion (CDF) Experience: Direct experience with CDF Data Objects and Knowledge Graph traversal.
- Familiarity with vector databases and RAG architectures
- Cloud-native development experience (AWS, Azure, GCP)
- Impact 2025
- Cognite's Industrial AI: Moonshot
- We’re globally recognized domain experts with an international presence that spans Phoenix, Houston, Oslo Tokyo, Bengaluru, and Abu Dhabi.
Skills Required
- 6–9 years of AI/ML engineering experience delivering production-grade AI features
- Hands-on experience with LLMs, embeddings, prompt engineering, and related AI techniques
- Experience building LLM-driven or agentic systems using LangChain or similar frameworks
- Ability to query and navigate complex relational and graph data models
- Full-stack engineering experience with React or similar, TypeScript, and Python, Kotlin, or similar backend technologies
- Industrial and supply chain data literacy, including time series and sparse metadata
- Ability to independently develop and iterate AI solutions from schemas and customer or synthetic data
- Experience using AI-powered coding assistants such as Cursor, Claude Code, or Copilot
- Strong communication and storytelling skills for technical and executive audiences
- Ability to write clean, maintainable, scalable code using testing, version control, and code review practices
- Supply chain expertise with SAP MM, PP, PM, or platforms such as o9, Kinaxis, or OMP
- Knowledge of ISA-95, ISA-88, SCOR, or related supply chain and manufacturing frameworks
- Experience with Cognite Data Fusion Data Objects and Knowledge Graph traversal
- Familiarity with vector databases and RAG architectures
- Cloud-native development experience with AWS, Azure, or GCP
Cognite Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Cognite and has not been reviewed or approved by Cognite.
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Affordable Benefits — Healthcare premiums for employees and dependents are often fully covered, reducing out‑of‑pocket costs. Feedback suggests this makes the total rewards feel competitive in key markets.
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Parental & Family Support — Paid parental leave for primary and secondary caregivers is described as generous. This signals a family‑friendly approach that many consider a standout perk.
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Leave & Time Off Breadth — Unlimited PTO with a company‑wide year‑end shutdown offers ample time away from work. Flexible time‑off options are consistently highlighted across U.S. roles.
Cognite Insights
What We Do
Cognite is an AI company that delivers industrial software to improve the production efficiency of Energy, Process Manufacturing, and other industrial companies. We deliver an Industrial DataOps platform that liberates siloed data and empowers our customers to solve some of their most complex business problems with AI-powered solutions. The typical solutions we enable drive innovative new ways to approach Data Exploration, Digital Operator Rounds, Production Optimization, Turnaround Planning, and Root Cause Analysis. We do this by automating and scaling industrial data contextualization of various sources (such as time series, engineering diagrams, equipment logs, maintenance records, 3D facility models, images, large point clouds, and more). We use AI and other tools to find and map the meaningful relationships between the data across these various sources. In addition, we provide intuitive tools that enable efficient use of analytics and automated workflows, as well as prebuilt AI capabilities and a low-code industrial agent builder, Cognite Atlas AI, that enables AI to carry out more complex operations with greater accuracy.
Why Work With Us
Employees at Cognite are pushing the envelope with the latest cloud technology, scaling industrial applications across hundreds of assets, revolutionizing industrial data models, and working with robotics. Cogniters are fast, creative, and resilient. We keep the energy high and fun, learning from our mistakes and celebrating our victories together.
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