Senior AI Platform Engineer, Agentic Systems & MCP
hace 2 días
Madrid
CommonShare is building the infrastructure for transparent, sustainable, and resilient supply chains. Our platform helps companies, suppliers, certification bodies, auditors, and ecosystem partners collaborate around traceability, sustainability, compliance, verified claims, product data, and supply-chain risk. We are building a collaborative lifecycle cloud for supply-chain data: one that can gather fragmented data from many parties, harmonize it into regulation-ready schemas, verify claims with evidence, power Digital Product Passports, support marketplace discovery, and help organizations comply with emerging regulations such as EUDR, ESPR, UFLPA, CBAM, CSRD, and DPP requirements. AI is becoming central to this mission. We are looking for an engineer who can help us build reliable, secure, production-grade agentic AI systems for complex supply-chain workflows. The Role We are hiring a Senior Platform Engineer to design and build agentic AI tools for CommonShare’s platform. This role sits at the intersection of backend engineering, AI systems, data architecture, workflow automation, and product engineering. You will build AI agents that can reason over supply-chain data, retrieve relevant evidence, call internal tools, interact with APIs, coordinate workflows, and help users complete complex compliance and traceability tasks. This is not a research-only role. We are looking for someone who can ship AI-native systems into production: systems that are reliable, observable, secure, auditable, and useful to real users. What You'll Do • Design and build production-grade agentic AI systems for traceability, sustainability, sourcing, compliance, and verified-claims workflows., • Build MCP-compatible tool servers that expose CommonShare capabilities to AI agents in a secure, permissioned, auditable way., • Develop tools and services that allow agents to query supply-chain graphs, retrieve documents, call APIs, update workflows, request supplier data, and generate compliance-ready outputs., • Create RAG pipelines over certifications, audit files, supplier records, product data, regulatory mappings, and evidence trails., • Design graph-based and semantic data models for companies, facilities, products, materials, suppliers, certifications, orders, claims, and regulations., • Build evaluation frameworks to measure agent accuracy, reliability, grounding, task completion, latency, cost, and safety., • Implement guardrails, permissions, access controls, audit logs, review queues, and human-in-the-loop approval flows., • Collaborate with product, design, data, sustainability, and customer-facing teams to turn messy real-world workflows into intuitive AI-powered features., • Prototype quickly, validate with users, and harden successful prototypes into scalable production systems., • Help define CommonShare’s AI platform architecture, especially around agent orchestration, MCP, retrieval, semantic models, and federated data access. Requirements • 5+ years of software engineering experience, with strong backend, full-stack, data, or AI platform experience., • Hands-on experience building production software with Python, TypeScript, Go, Rust, Ruby, or similar languages., • Experience building with LLMs, tool calling, agent frameworks, RAG systems, embeddings, or semantic search., • Strong understanding of APIs, distributed systems, data modeling, testing, reliability, and observability., • Experience working with structured and unstructured data, including documents, tables, APIs, databases, and search systems., • Ability to design secure systems with authentication, authorization, role-based access, audit logs, and data privacy controls., • Strong product judgment: you care about building useful AI systems that solve real user problems, not demos that only work in ideal conditions., • Comfort operating in a fast-moving startup environment with ambiguity, ownership, and high technical standards., • Interest in sustainability, supply chains, traceability, procurement, compliance, marketplaces, or climate-related data., • Experience with MCP, including building MCP servers, tools, resources, prompts, or client integrations., • Experience with LangGraph, LlamaIndex, OpenAI APIs, Anthropic Claude, Semantic Kernel, AutoGen, CrewAI, or similar AI orchestration tools., • Experience with vector databases such as Pinecone, Weaviate, Milvus, Qdrant, pgvector, Elasticsearch/OpenSearch, or similar systems., • Experience with graph databases such as Neo4j or similar graph/relationship-oriented data stores., • Experience with ontologies, semantic models, knowledge graphs, entity resolution, or data harmonization., • Experience with document intelligence, OCR, PDF parsing, extraction pipelines, validation workflows, or evidence management., • Experience with federated data systems, decentralized architectures, data sovereignty, connectors, or policy-based exchange., • Experience building compliance, procurement, ESG, supply-chain, sustainability, certification, marketplace, ERP, PLM, or RegTech systems., • Experience with geospatial data, satellite data, physical tracker data, or risk intelligence. What Success Looks Like • In your first few months, you will have shipped agentic workflows that reduce manual work for CommonShare users or internal teams., • You will have helped define the MCP/tool architecture for how agents safely interact with CommonShare systems., • You will have built retrieval and reasoning capabilities over real supply-chain, product, facility, supplier, certification, or compliance data., • You will have established evaluation and observability patterns so AI outputs can be measured, improved, and trusted., • You will have helped move CommonShare from AI-assisted workflows toward reliable, auditable, multi-agent automation.
