AI Platform Engineer (Hybrid work schedule)
$121,316–$163,720 year
Hybrid · Illinois, United States
Job Summary
AI Platform Engineer to bridge our data platform and AI ecosystems, integrating a GCP-based data warehouse with a semantic layer and AI-driven applications. Responsibilities include building and refining integrations between the GCP data warehouse, semantic layer, and downstream systems; developing or modifying APIs to enable data and services for AI-driven applications; creating connectors and lightweight services for programmatic interaction; working with data models/datasets for analytics and AI use cases; troubleshooting data movement and system reliability/performance; collaborating with data, platform, and application teams to understand requirements and unblock use cases; and contributing to pragmatic design decisions to improve interoperability without over-engineering. Required capabilities include Python (preferred), SQL, API development (REST/gRPC), orchestration tools (Airflow), production-grade data pipelines, cloud data platforms (GCP), ML/AIystems experience, data warehousing and data modeling knowledge, governance-focused architecture, strong communication skills, enterprise data platform familiarity, infrastructure-as-code/DevOps familiarity, and semantic/metrics layer experience.
Required Qualifications
- strong programming skills (Python preferred)
- strong SQL skills
- experience building APIs (REST, gRPC, or similar)
- experience with orchestration or workflow tools (such as Airflow)
- experience building and maintaining production-grade data pipelines
- experience working with cloud data platforms (GCP preferred)
- experience supporting ML- or AI-driven systems
- familiar with data warehouses and core data modeling concepts
- systems-thinking mindset with attention to governance, abstraction, and architectural clarity
- strong communication skills with the ability to articulate technical concepts to technical and non-technical stakeholders
- experience with enterprise data platforms
- familiar with infrastructure-as-code or modern DevOps practices
- familiar with semantic or metrics layers
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