Lead AI Engineer - 453451

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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 workstreamtranslating 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)

Field-Based Quality Engineering & Diagnostics - (20%):\

• 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)

Function-Specific Model Optimization & Adaptation - (15%):\

• 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)

Client Success & Implementation Leadership -(10%):\

• Serve as t e 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 through hands-on workshops, knowledge transfer sessions, and executive-level capability demonstrations. (Lead/Execute)\
• Identify expansion opportunities through deep understanding of client\'s technical landscape and business challenges. (Lead)\
• Communicate complex technical concepts effectively to diverse audiences from C-suite to implementation teams. (Lead)

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\

• Spans agent design, evaluation rigor, integration complexity, and cross-team delivery and deep business/domain expertise under evolving constraints.

Cross-Functional Interactions:\

• Continuous interaction with domain transformation leads, platform/SRE, security, and enterprise app teams

Compensation and Benefits:\

• The expected