About the Role:
As a CBRE Systems Engineer Principal - Cloud & AI Engineering, you will be one of the most technically senior individual contributors within Cloud Platform Services, bringing deep, hands-on expertise at the intersection of data engineering, applied AI/ML, and cloud-native software development. You design, build, and operate the intelligent data platforms, AI/ML services, and cloud automation capabilities that power CBRE's cloud engineering and FinOps organization - building the systems that engineers and operators across CBRE depend on to understand their cloud estate, reduce costs, and scale cloud operations efficiently across AWS, Azure, and GCP. This is a principal-level individual contributor role. You are a practitioner who goes deep. You own the most complex, highest-impact data and AI engineering problems - designing large-scale data pipelines, training and deploying production ML models, building generative AI capabilities, and developing the cloud-native services that bring all of it together. You work independently on open-ended problems, collaborate closely with cloud architects and senior engineers, and set the technical bar for the data and AI engineering discipline across the team. You apply extensive, diversified knowledge of data engineering, AI/ML, and cloud-native development principles to solve complex, multi-dimensional platform challenges without direct supervision.
What You'll Do:
Cloud Engineering & Platform Development
- Design, build, and maintain large-scale data pipelines that ingest, normalize, transform, and serve cloud usage, billing, operational, and telemetry data across AWS, Azure, and GCP - using modern ELT/ETL patterns, orchestration frameworks (Apache Airflow, Azure Data Factory, or equivalent), and transformation tools including dbt.
- Architect and implement data lakehouse solutions using Apache Iceberg or Delta Lake, building reliable, versioned, ACID-compliant data layers that support both analytical and AI/ML consumption patterns at enterprise scale.
- Develop distributed data transformation and processing workloads using Apache Spark or equivalent, optimizing for performance, cost-efficiency, and maintainability across large cloud billing and operations datasets.
- Build modular, reusable data components - shared transformation functions, canonical data models, and governed data APIs - that reduce duplication and provide consistent, lineage-tracked access to cloud platform data across internal consumers.
- Design and implement data quality frameworks: automated validation, freshness monitoring, anomaly alerting, and schema evolution strategies that ensure downstream AI/ML models and analytical consumers receive trustworthy data.
- Investigate and resolve the most complex data pipeline, quality, and system issues - applying in-depth knowledge of cloud data service architectures to diagnose and permanently fix root causes.
AI/ML Engineering & Intelligent Platform Capabilities
- Design, develop, train, and deploy production machine learning models that serve core Cloud Platform Services use cases: cloud spend anomaly detection, predictive cost optimization, resource rightsizing recommendations, reserved instance/savings plan modeling, and intelligent incident triage.
- Engineer the full ML lifecycle end-to-end: feature engineering from raw cloud telemetry and billing data, model selection and experimentation, hyperparameter tuning, validation, deployment, and ongoing performance monitoring.
- Build and maintain MLOps pipelines using MLflow or equivalent - covering model versioning, automated retraining triggers, A/B testing infrastructure, drift detection, and the governance standards required to keep production AI systems auditable and reliable.
- Develop and integrate generative AI capabilities into CBRE's Cloud Assistant and cloud platform tooling - building LLM-powered interfaces, retrieval-augmented generation (RAG) architectures, and agentic automation workflows that give engineers natural language access to cloud data, insights, and operational actions.
- Evaluate and apply LLM APIs (Azure OpenAI, OpenAI, Google Gemini, AWS Bedrock) and agentic AI frameworks (LangChain, AutoGen, or equivalent) to cloud engineering workflows such as AI-assisted infrastructure generation, natural language cost querying, automated runbook execution, and intelligent alert correlation.
- Document model design, feature engineering decisions, evaluation results, and known failure modes - ensuring AI/ML work is reproducible, reviewable, and transferable across the engineering team.
Cloud-Native Software Engineering & Microservices
- Design and build production-grade microservices and REST APIs that expose data and AI capabilities as reliable, versioned, observable services - enabling CBRE's internal platforms and engineering teams to consume intelligence and automation programmatically.
- Develop cloud-native platform features and self-service capabilities that integrate with AIDP (Automated Infrastructure Deployment Platform), ECMP (Enterprise Container Management Platform), Cloud Assistant, and Innovation Studio - extending the platform's reach and reducing operational toil across Cloud Platform Services.
- Build event-driven automation pipelines using cloud-native messaging and serverless tooling - processing real-time telemetry, cost signals, and operational events to trigger intelligent recommendations or automated remediation actions.
- Apply software engineering best practices across all platform work: clean interfaces, unit and integration testing, API versioning, containerization (Docker/Kubernetes), CI/CD pipelines, structured logging, and distributed tracing.
- Identify and refactor legacy platform components - improving modularity, reducing technical debt, and raising the engineering quality of the systems the team maintains and the broader engineering organization depends on.
Cloud Infrastructure & Platform Automation
- Build and maintain infrastructure-as-code for the data and AI platform: cloud compute, storage, networking, and managed AI/ML services provisioned via Terraform or Bicep, following IaC standards for modularity, version control, and CI/CD delivery.
- Design and operate Kubernetes workloads (AKS, EKS, or GKE) for data processing and AI/ML inference - including cluster configuration, workload scheduling, autoscaling, resource governance, and container security hardening.
- Develop cloud automation for data and AI platform operations: environment provisioning, model deployment pipelines, data environment lifecycle management, and cost guardrails that enforce budget boundaries automatically.
- Apply extensive knowledge of cloud-native data and AI service architectures across Azure (Synapse / Fabric, Azure ML, Azure Data Factory, ADX), AWS (Glue, Athena, SageMaker, Bedrock), and GCP (BigQuery, Vertex AI, Dataflow) to design platform solutions that are cost-effective and operationally sound.
Data & AI Governance, Quality & Engineering Standards
- Design and implement data governance frameworks: lineage tracking, data cataloging, column-level security, RBAC, retention policies, and audit logging - ensuring the data platform is compliant, trustworthy, and audit-ready.
- Implement AI/ML governance practices: model explainability, bias evaluation, prediction logging, and the documentation standards that keep the team's AI systems transparent and responsible.
- Define and uphold engineering standards for the data and AI discipline: code quality, testing coverage, peer review expectations, CI/CD requirements, and documentation standards - modeling the level of craft expected from the team.
- Apply a multi-dimensional, conceptual, and innovative approach to solving the most complex data and AI platform problems. Identify creative solutions without direct supervision and communicate technical decisions clearly to engineers and architects at all levels.
- Lead by example and model behaviors consistent with CBRE RISE values - Respect, Integrity, Service, and Excellence.
What You'll Need:
Education & Experience
- Bachelor's Degree preferred with 10-15 years of relevant experience. In lieu of a degree, a combination of experience and education will be considered.
- 7+ years of hands-on data engineering experience in production environments - designing and operating large-scale pipelines, data lakehouses, and governed data platforms at enterprise scale.
- 4+ years of applied AI/ML engineering experience with a demonstrated track record of shipping production models and ML-powered capabilities into real operational systems.
- 3+ years of cloud-native software engineering experience building microservices, APIs, or platform components in a multi-cloud environment (Azure, AWS, or GCP).
- Demonstrated experience delivering generative AI or LLM-based capabilities into production - including RAG architectures, agentic workflows, or AI-powered user-facing tooling.
Technical Skills
- Data engineering: expert proficiency with modern data platform patterns - ELT/ETL pipeline design, dbt (models, tests, macros, packages), Apache Spark or Databricks, orchestration (Airflow, Azure Data Factory, or equivalent), Delta Lake / Apache Iceberg, advanced SQL, and data lakehouse architecture at scale.
- AI/ML engineering: deep hands-on proficiency in the Python ML ecosystem (scikit-learn, XGBoost, LightGBM, statsmodels, Prophet or equivalent time-series tooling); experience with model training, evaluation, deployment, and serving; MLOps proficiency with MLflow or equivalent platforms.
- Generative AI & LLMs: strong working knowledge of LLM APIs (Azure OpenAI, OpenAI, Google Gemini, AWS Bedrock, or equivalent), RAG pipeline design (vector stores, embedding strategies, retrieval optimization), agentic AI frameworks (LangChain, AutoGen, Semantic Kernel, or equivalent), and prompt engineering techniques.
- Cloud platforms: multi-cloud depth across Azure (Synapse Analytics / Microsoft Fabric, Azure ML, Azure Data Factory, Azure Data Explorer, Azure OpenAI), AWS (Glue, Athena, SageMaker, Bedrock, Redshift), and GCP (BigQuery, Vertex AI, Dataflow, Cloud Composer); comfortable designing and operating cloud-native data and AI service architectures.
- Microservices & APIs: production experience building REST APIs and microservices in Python (FastAPI, Flask) or equivalent; strong understanding of service design, API versioning, fault tolerance, containerization, and observability (structured logging, distributed tracing, Prometheus/Grafana or equivalent).
- Infrastructure-as-code: working proficiency with Terraform and/or Bicep sufficient to provision and manage cloud infrastructure for data and AI workloads; experience with CI/CD pipeline design (GitHub Actions, Azure DevOps, or equivalent).
- Containers & orchestration: production experience with Kubernetes (AKS, EKS, or GKE) for running data and AI workloads; proficiency with Docker, Helm, and Kubernetes resource management.
- Data & AI governance: hands-on experience with data lineage tools (OpenLineage, Apache Atlas, Purview, or equivalent), data catalogs, column-level security, RBAC, and model governance practices including explainability and prediction auditing.
Competencies
- In-depth expertise in leading-edge data engineering, AI/ML, and cloud-native development techniques - able to identify and solve the most complex platform engineering problems independently.
- Multi-dimensional, conceptual, and innovative thinking - designs creative, pragmatic solutions to data and AI platform challenges with minimal direction, and clearly articulates the trade-offs behind each decision.
- Strong collaboration and communication skills - comfortable working alongside engineers and architects at all levels, and able to explain complex data engineering and AI/ML decisions clearly to non-technical stakeholders.
- Engineering rigor and ownership mentality - takes personal accountability for the reliability, correctness, and maintainability of systems built and deployed, and drives the team toward higher quality.
- Expert organizational skills with an unrivaled inquisitive mindset. In-depth knowledge of Microsoft Office products including Word, Excel, and Outlook.
Preferred Qualifications
- Cloud AI or data certifications across at least one provider (e.g., Azure AI Engineer Associate, Azure Data Engineer Associate, AWS Machine Learning Specialty, GCP Professional Data Engineer, or equivalent).
- Cloud architecture certifications (e.g., Azure Solutions Architect Expert, AWS Solutions Architect Professional, GCP Professional Cloud Architect).
- Databricks certifications (e.g., Databricks Certified Data Engineer Professional or Databricks Certified Machine Learning Professional).
- FinOps Certified Practitioner (FOCP) designation from the FinOps Foundation.
- Experience contributing to or building an internal data or AI platform that serves multiple engineering or business teams - including experience with self-service data access patterns and platform documentation.
