Senior AI Engineer

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.

• 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 foragentic 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., Type Script/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.

• Guide the design and development of solutions built with:
• Frontend:
  React, Next.js, Type Script, 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.

• Enterprise-grade CI/CD (Azure Dev Ops,ArgoCD)
• Automated testing, contract testing, and quality gates
• Observability platforms (Prometheus,Open Telemetry, 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 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 s, 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

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 standard sin coding, cloud security, API governance, and data interoperability.

Partner with Product, Architecture, Operations, and Client Technology to prioritize work, unblock…