Senior AI Engineer

Job Title Senior AI Engineer Job Description Summary Build the next generation of scalable, AI-powered software with modern tools, cloud-native systems, and world-class engineering practices. Cushman & Wakefield is investing heavily in modernizing and transforming its global technology platforms — and we are looking for a Sr AI Engineering Lead who wants to own technical direction end to end. AI is the primary differentiator here, not a side feature: you will define how intelligence is embedded across products built on top of a complex, multi-source enterprise data estate spanning a global commercial real estate firm (properties, leases, work orders, capital projects, and occupier analytics). If you're motivated by technical breadth, architectural influence, and building elite engineering teams — you'll thrive here.

Technology Strategy & Vision     - Define multi-year architectural direction across full-stack systems, cloud platforms, and AI-driven products — focusing on scale, resilience, security, and global performance.   -   Establish   standards for   agentic workflows, intelligent search, conversational interfaces, and data-grounded reasoning across CW's enterprise platforms.   - Shape engineering roadmaps using best-in-class development stacks (e.g., TypeScript/Node.js, Python, Go, C#, React, Next.js, microservices, event-driven architectures).   - Foster a modern engineering culture emphasizing automation, craftsmanship, clean architecture, and continuous improvement.   ---   Hands-On Full-Stack & Platform Engineering Leadership   Guide the design and development of solutions built with:   - Frontend: React, Next.js, TypeScript, Tailwind, micro-frontends   - Backend: Node.js, Python ( FastAPI ), Go, .NET Core, event-driven microservices   - Cloud & Infra: Azure (preferred), AWS, Kubernetes (AKS/EKS), serverless architectures, Terraform   - Data & AI:   Databricks (Genie/AI-BI, Mosaic AI Agent Framework, Unity Catalog, Delta Lake, medallion architecture, model serving),   Azure Data Lake, vector databases, LLM orchestration and agentic frameworks   - APIs:   GraphQL ,   gRPC , REST at scale   Drive innovation by embedding AI/ML, automation,   production prompt engineering (system prompt architecture, context injection, business-rule encoding,   citation   and grounding patterns),   intelligent workflows, and data interoperability into core products.   ---    Execution, Quality & Delivery Excellence   Champion excellence through best practices:   - Enterprise-grade CI/CD (Azure DevOps,   ArgoCD )   - Automated testing, contract testing, and quality gates   - Observability platforms (Prometheus,   OpenTelemetry , Grafana, New Relic, Datadog)   - Secure-by-design principles, threat modeling, zero trust patterns   Oversee engineering delivery across multiple squads — ensuring consistency, velocity, and reliability.   ---   AI Engineering Standards   Define and enforce AI engineering standards across products and external delivery teams:   - AI evaluation framework design — golden-question sets, hallucination measurement, regression testing, and model quality gates for production AI systems   - Pre-semantic data contracts — schema stability requirements,   canonical   entity ID definitions, and data quality thresholds that AI systems can depend on   - AI governance — access control patterns for confidential and client data, responsible use guardrails, and auditability requirements for AI-generated outputs   - LLM system design standards — prompt versioning, context window management, grounding and citation patterns, and failure-mode handling   ---   Architecture, Governance & Platform Integrity   Lead architectural reviews and key design decisions across services, domains, and integrations.   Establish patterns for scalability, fault tolerance, global data compliance, and cost optimization.   Maintain   high standards   in coding, cloud