As a Senior AI Systems Engineer, you will design and build production-grade AI systems that integrate AI models, automation platforms, enterprise data, and workflow orchestration into cohesive, scalable solutions. You will translate architectural direction into working systems that embed reusable intelligence capabilities across business processes.
This role sits within the Operational AI organization, which focuses on embedding AI capabilities into core enterprise workflows. You will design and integrate AI-enabled components within established reference architectures and governance standards, ensuring solutions align with enterprise data context, semantic consistency, and responsible automation principles.
The focus of this role is on engineering and operating production AI systems, including backend services, orchestration layers, and enterprise integrations—not on research or experimental model development.
This position will offer flexibility for hybrid work schedules to include both in-office presence and telecommute/virtual work, to be based from either Houston or Dallas, TX.
Key Responsibilities
- Design and implement end-to-end AI-enabled workflows that integrate LLMs, machine learning services, document understanding tools, and automation platforms into production-ready enterprise solutions
- Develop orchestration layers that clearly separate decision logic from execution, enabling modular and reusable intelligence capabilities
- Build backend services, APIs, and event-driven integrations that expose AI capabilities across multiple enterprise systems
- Partner with enterprise data teams to ensure AI systems align with canonical data models, shared definitions, and governed semantic layers
- Process and structure structured and unstructured inputs into validated, traceable, and policy-compliant data representations for use by AI systems
- Design and implement feature engineering pipelines and contextual enrichment patterns to support operational AI use cases
- Integrate AI services into enterprise workflow and automation platforms while adhering to DevSecOps and enterprise security standards
- Embed explainability, traceability, monitoring, and observability into AI-enabled systems
- Contribute to drift detection, performance monitoring, and responsible AI lifecycle management
- Evaluate and communicate technical trade-offs related to integration complexity, data alignment, performance, and operational reliability