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- Belsize Park, England, United Kingdom
- Belsize Park, England, United Kingdom
Über
Define and evolve cross‑team architecture for data platforms and services in the AEC Platform Data organization. Establish standards, reference architectures, and reusable components; drive adoption across teams in the AEC Platform Data organization. Lead architectural governance and architectural decision records (ADRs); run design reviews across teams. Design and guide implementation of distributed systems and data platforms (APIs, schemas, event streams; batch and streaming). Ensure reliability, security, and performance; define SLOs and drive observability (metrics, tracing, logging). Partner across the AEC organization's product and platform teams to align roadmaps and integrations. Communicate architectures with clear views and diagrams (e.g., C4) and executive‑ready narratives. Qualifications
Bachelor's degree in computer science, or equivalent experience. 10+ years as a Software Architect in data‑intensive cloud environments. Experience with cloud services, API design, database architecture, big data tools and frameworks. Strong understanding of data modeling, management and processing principles. Excellent knowledge of software design and architecture. Demonstrated ability to influence without authority and drive cross‑team alignment. Mastery of taking complex ideas and conveying them in a concise and impactful manner. Excellent verbal, written communication, and presentation abilities to effectively communicate software architecture strategy to a variety of stakeholders. Ability to collaborate with a global team. Experience in the Architecture, Engineering, and Construction industry. Cloud services experience with AWS strongly preferred (EC2, ECS, Lambda, API Gateway, S3, DynamoDB, RDS). Database architecture and technologies: Snowflake, relational, NoSQL. Event‑driven and streaming architectures: Kafka or Kinesis; exactly‑once processing; schema evolution (Avro/Protobuf). API and service design: REST, gRPC, GraphQL; versioning and backward compatibility at scale. Distributed systems and data platforms: microservices, service mesh, event‑driven architecture, streaming processing. Observability/SRE: OpenTelemetry, distributed tracing, metrics/SLOs for data services. Knowledge graphs/semantic modeling (nice‑to‑have): RDF/OWL, property graphs, or feature stores for ML. Experience designing and integrating AI systems and agents in enterprise environments; familiarity with MCP and tool/plugin ecosystems. Hands‑on with RAG architectures: embeddings, vector stores, chunking, re‑ranking, and retrieval evaluation. Experience with agentic orchestration frameworks (e.g., LangGraph, Semantic Kernel) and function/tool calling; Assistants‑style APIs. Data governance, privacy, and safety for AI systems (PII handling, prompt‑injection defenses, content filtering, auditability). LLM observability and evaluation: offline/online evals, guardrails, prompt/version management and telemetry (e.g., LangSmith, PromptFlow, OpenTelemetry). Domain‑specific AI for AEC (nice‑to‑have): extracting structure from BIM/IFC and construction docs; CAD/point‑cloud processing.
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