AI Engineer (Supply Chain)

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 an 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 and Inbound/Outbound Logistics (Shipments/Deliveries), suppliers and customers and external market and 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, 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.

Responsibilities:

• 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.

Requirements:

• 5-8 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.

Bonus Skills (The "Fast-Track" Profile):

• 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).