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.

Job Description 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., 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.

--- Hands-On Full-Stack & Platform Engineering Leadership 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.

---  Execution, Quality & Delivery Excellence Champion excellence through best practices:

- 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 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 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 --- 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…