Lead AI Engineer

Overview As a Forward Deployed AI Engineering Lead 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 agentic modules and integration patterns (MCP/tooling), establish strong evaluation and regression discipline, and drive adoption by embedding within transformation teams and BU and partnering with 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 BU Facing Solution Architecture & Implementation - (40%):

Architect and deploy transformative AI agent solutions directly in client environments,adapting core technologies to unique client constraints and infrastructure. (Lead/Execute) Rapidly customize agent patterns (tool integrations, enterprise system connections, security models) to solve high-impact business challenges across diverse client tech stacks. (Lead/Execute) Transform ambiguous client requirements into production-ready solutions with minimal iterations through exceptional technical discovery skills. (Lead) Drive on-site performance optimization beyond client expectations (latency, reliability,throughput) while working within client infrastructure limitations. (Execute/Lead) Establish implementation playbooks that accelerate future deployments and enable customer success teams to scale. (Lead)

Design and implement BU and function-specific evaluation frameworks that validate solution effectiveness in production environments with real data. (Lead/Execute) Develop rapid diagnostic methodologies to identify and resolve critical issues during implementation without disrupting client operations. (Execute/Lead) Create monitoring systems that provide early warning of edge cases and performance degradation unique to each deployment environment. (Execute) Perform advanced troubleshooting in constrained client environments where standard toolsmay be unavailable. (Execute/Lead) Establish quality baselines that enable clients to self-monitor system health post- implementation. (Execute/Lead)

Fine-tune model selection and routing strategies based on function-specific data characteristics and performance requirements. (Lead/Execute) Optimize prompt engineering for unique BU domains, creating specialized techniques that overcome domain-specific challenges. (Execute/Lead) Implement model adaptation techniques that improve performance with minimal additional client data. (Execute) Develop function-ready evaluation frameworks that demonstrate model effectiveness to technical and business stakeholders. (Lead)

Enterprise Systems Integration & Data Flow Engineering - (15%):

Lead complex integrations between AI capabilities and diverse client systems (ERPs, CRMs, legacy databases, custom applications). (Lead/Execute) Design and implement secure data pipelines that respect client compliance requirements while enabling AI functionality. (Execute/Lead) Create adapter patterns that isolate core AI functionality from client-specific integration complexities. (Lead) Develop client-specific documentation and knowledge transfer protocols that enable client teams to maintain integrations independently. (Execute/Lead)

Serve as the primary technical bridge between core engineering and client stakeholders, translating between business needs and technical capabilities. (Lead) Mentor client technical teams to build internal AI implementation capabilities. (Lead) Drive adoption