Responsabilidades

- Work directly alongside strategic customers to discover high-value workflows, model the data those workflows depend on, and build the AI systems for their Design & Make workflows to enable customer’s measurable business outcomes.

- Move fluidly from understanding a customer's workflow and architecture to writing code.

- Spend weeks writing production code to build agents and orchestration, retrieval and graph services, connectors and APIs - inside customer environments, alongside customer engineers.

- Ship concrete deliverables: AI agents, MCP servers and tools, domain and semantic models, knowledge-graph extensions, retrieval and context-resolution pipelines, integrations into ERP, PLM, BIM, enterprise data platforms, and eval harnesses to move new capabilities into production.

- Work deeply inside customer environments, connecting technology and data with the rest of the enterprise.

- Translate insights developed through customer work into reusable agents, connectors, reference architectures, engineering patterns, and direct product feedback that shapes organization's platform roadmap.

- Operate independently when the problem is not yet well defined.

- Travel up to 75% during active engagements, including extended periods working onsite with customers.

- Work with one customer at a time on focused engagements, for specified duration, on-site at customer locations that may be outside your home country, with travel of up to 75%, subject to any immigration or other requirements (averages likely to be lower across the whole year).

- Remain an organization employee throughout, with the intention that organization's policies, benefits, and time off apply on every assignment.

- Comply with some customers' onboarding, safety, security or other requirements,



which will be discussed at the time.

Minimum Qualifications

- 6+ years building and operating production software, including 2+ in a customer-facing or embedded role (forward-deployed or founding engineer).

- CS/Engineering degree or equivalent hands-on experience.

Habilidades requeridas

- Full-stack range.

Expert

Python plus one of TypeScript, Java, or C# - comfortable across backend, data pipelines, integrations, and a lightweight React surface when a workflow needs one.

- Cloud fluency in Azure or AWS: containers, serverless, managed data stores, and identity and networking inside customer-governed environments (e.g., infrastructure as code with Terraform, Bicep, or CloudFormation), with strong API, distributed-systems, CI/CD, and observability fundamentals.

- Agentic systems in production. You've built and run single- and multi-agent systems - tool schemas, memory, human-in-the-loop checkpoints - on a modern orchestration framework (e.g., LangGraph, LangChain, Bedrock AgentCore, etc), MCP making practical cost and latency trade-offs (e.g., model selection, caching).

- Evaluation as engineering. You build golden datasets and eval harnesses from real customer data, and you measure groundedness, task success, and retrieval quality using standard tooling (e.g., RAGAS, LangSmith, Langfuse).

- Retrieval and data readiness. You've built retrieval systems end-to-end (chunking, embeddings,



hybrid search, reranking) over a vector store and can assess and fix messy enterprise data - lineage, permissions, entity resolution, schema mapping.

- Knowledge architecture for AI. You model domain semantics into AI-ready context - ontologies, semantic layers, knowledge graphs - and ground agents in it via GraphRAG or equivalent, using at least one graph technology (e.g., Azure Cosmos DB Gremlin, Amazon Neptune, Neo4j, or equivalent) and its query language (e.g. Gremlin, Cypher, SPARQL).

- Ownership under ambiguity. You take greenfield customer problems from framing to production and measured outcomes and can explain a non-deterministic system with equal credibility to an engineer and an executive.

Habilidades preferidas

- Preferred QualificationsDomain fluency in AECO, with the ability to ramp quickly on design-to-construction-to-operations processes.

- Platform fluency: Platform Services (APS), AEC Data Model API, Construction Cloud and Common Data Environment (CDE) concepts, Forma, Fusion, Flow, and data models.

- Enterprise systems of record integration: ERP, PDM, PLM, BIM, project controls, cost, scheduling, and asset management, plus enterprise data platforms (data lakes, warehouses, Lakehouse, analytics platforms).

- Constrained enterprise deployment: customer VPCs, private, on-prem, or air-gapped environments, data residency and sovereignty requirements, SSO/SCIM, and formal security review processes.

- AI-native builder practice: routine use of AI coding agents, harnesses, and automation to multiply individual and team throughput, including spec-driven development: writing clear specs and executable acceptance criteria for coding agents, keeping changes small and

📌 Principal Embedded Engineer, AI Solutions (Barcelona)
🏢 JOIND
📍 Barcelona