Senior AI Engineer

Overview As a Senior AI Engineer specializing in Agentic AI enablement, you will lead the design and delivery of production-grade agent capabilities built on the enterprise AI Backbone across cloud and edge environments – across supply-chain and global functions. You will own end-to-end delivery of key agent modules and integration patterns (MCP/tooling), establish strong evaluation and regression discipline, and drive adoption by partnering with transformation teams, BU, platform engineering, and enterprise application owners. You serve as a technical anchor for the workstream—translating ambiguous business workflows into measurable agent outcomes, proactively identifying risks, proposing options/tradeoffs, and ensuring solutions scale across domains.

Responsibilities Architectural Leadership & Strategic Execution (40%)

• Design and architect transformative agent systems that enable organization-wide scaling, establishing new paradigms in agent architecture that become company standards. (Lead/Execute)
• Pioneer novel agent patterns (tool-use orchestration, multi-agent systems, advanced memory architectures) that dramatically improve performance across the enterprise. (Lead/Execute)
• Transform ambiguous business problems into elegant technical solutions with 10x efficiency gains through innovative approaches to system design. (Lead)
• Optimize critical performance metrics beyond standard benchmarks, creating breakthrough improvements (90th percentile latency reduction, 50%+ token efficiency, near-perfect tool-call reliability). (Execute/Lead)
• Establish architectural governance that propagates excellence across teams and projects. (Lead)

Advanced Evaluation & Quality Engineering (20%)

• Design scientifically rigorous evaluation frameworks that uncover non-obvious failure modes and edge cases others miss. (Lead/Execute)
• Create organization-level evaluation standards and platforms that scale across multiple teams and projects. (Lead)
• Innovate on automated testing methodologies that dramatically increase code quality while reducing QA overhead. (Execute/Lead)
• Perform sophisticated statistical analysis of system behaviors to predict quality issues before they manifest. (Execute)
• Establish early warning systems for emerging failure patterns. (Execute/Lead)

Model Architecture & Routing Innovation (15%)

• Architect intelligent routing systems that autonomously optimize for cost, latency, and quality trade-offs. (Lead/Execute)
• Pioneer novel approaches to model selection, fine-tuning, and prompt engineering that set new performance standards. (Lead)
• Create optimization algorithms that continuously improve routing decisions based on real-time feedback loops. (Execute/Lead)
• Develop proprietary techniques for model evaluation that provide competitive advantage. (Execute/Lead)

Advanced Integration & Ecosystem Development (15%)

• Design scalable integration architectures that become enterprise standards for AI/app connectivity. (Lead)
• Create abstraction layers that dramatically simplify how teams connect AI capabilities to enterprise systems. (Execute/Lead)
• Establish next-generation integration patterns that anticipate future technology directions and enable seamless adoption. (Lead)
• Develop tooling that accelerates integration velocity across the entire organization. (Execute/Lead)

Organizational Multiplier & Innovation Leadership (10%)

• Serve as technical visionary, elevating the entire AI organization’s capabilities through knowledge transfer and mentorship. (Lead)
• Anticipate industry shifts and position the organization to capitalize on emerging technological opportunities. (Lead)
• Create internal communities of practice that accelerate knowledge sharing and collective innovation. (Lead)
• Represent the company’s technical excellence externally through publications, speaking engagements, and industry contributions. (Lead)
• Drive cross-functional initiatives that break down silos and create new organizational capabilities. (Lead/Execute)

Decision-Making Autonomy High-moderate — significant autonomy in AI engineering design choices and evaluation approach; aligns with standards and escalates policy/security-impacting decisions.

Supervision Required: Moderate-low — general direction from Transformation and Tech Executives and SME; self-directed execution with periodic design, execution and RoI reviews.

Complexity of Role: High — s…