Data & AI Delivery Leader

Job description

Be a part of our mission! As a world leader in creating comfortable, sustainable, and efficient climate solutions for buildings, homes and transportation, it's our responsibility to put the planet first. For us at Trane Technologies, and through our businesses including Trane® and Thermo King, sustainability is not just how we do business—it is our business. Do you dare to look at the world's challenges and see impactful possibilities? Do you want to contribute to making a better future? If the answer is yes, we invite you to consider joining us in boldly challenging what's possible for a sustainable world. Learn about our benefits designed for you to Thrive at work and at home. We boldly go. **Where is the work:** Monday to Thursday, work onsite with your colleagues. Fridays, choose your work location, balancing what your work requires. Role Summary We are seeking a Data & AI Delivery Leader to provide architecture leadership and delivery direction for strategic enterprise Data and AI initiatives. This role combines the technical depth of a solution architect with the execution ownership of a delivery leader, with equal accountability for enterprise data foundations, analytics architecture, and scalable AI solution delivery, while providing hands-on architecture leadership in key solution areas. The person will shape end-to-end solution direction, align work to enterprise standards, and guide delivery across cross-functional teams to ensure scalable, secure, high-quality outcomes. This is a leadership role for someone who can translate business needs into practical architecture and execution plans while providing continuity across initiatives spanning data platforms, analytics, semantic layers, AI/ML enablement, generative AI, knowledge retrieval, and intelligent automation. The role is intentionally balanced to lead both trusted data capabilities and AI-enabled business outcomes, while also building long-term team capability through mentoring, coaching, and practical knowledge transfer. Key Responsibilities 1. Solution Architecture & Technical Leadership - Own end-to-end architecture for Data and AI solutions, providing hands-on architecture leadership aligned with enterprise strategy, business goals, and platform standards. - Define scalable, secure, and reusable architecture patterns across data ingestion, transformation, modelling, semantic layers, analytics consumption, AI/ML enablement, and generative AI solution components. - Translate business capability needs into architecture blueprints, technical decisions, and implementation direction. - Establish and maintain reference architectures, design guardrails, and reusable solution patterns. - Ensure solutions are designed for performance, maintainability, interoperability, supportability, and long-term scale. 2. Delivery Leadership & Execution Oversight - Serve as the delivery leader for solution execution, partnering across product, engineering, governance, security, and business teams from concept through implementation while maintaining reasonable direct involvement in solution shaping and critical execution decisions. - Shape new initiatives by defining scope, delivery approach, sequencing, dependencies, risks, operating assumptions, and high-level estimates. - Drive architecture and delivery decisions across multiple concurrent efforts, balancing speed, quality, risk, sustainability, and value realization. - Help structure work across core and flex capacity, aligning the right skills and teams to the highest-value priorities. - Support release planning, solution reviews, issue resolution, change management, and adoption planning to ensure successful implementation while staying close enough to help unblock critical issues when needed. 3. Data Architecture, Analytics & AI Solution Design - Define architecture patterns across cloud-native data platforms, including data lake, warehouse, semantic, and consumption layers. - Guide conceptual, logical, physical, dimensional, and semantic modelling across enterprise data domains. - Ensure data structures, metrics, and semantic models are reusable, governed, and aligned to business definitions. - Partner with data engineers, AI engineers, BI developers, platform teams, and business stakeholders to design scalable data products and AI-enabled solutions. - Ensure governed data foundations support reporting, self-service analytics, operational insight, AI/ML workloads, and future extensibility, with active hands-on participation in key data and analytics decisions. 4. Enterprise AI Solution Leadership - Lead design and delivery of enterprise AI solutions, including generative AI, retrieval-augmented generation, enterprise AI assistants, knowledge retrieval, intelligent workflow automation, and predictive analytics use cases. - Define reusable AI architecture patterns covering prompt orchestration, model integration, retrieval design, vector-based knowledge access, tool integration, and human-in-the-loop controls. - Guide model and solution design decisions based on business value, feasibility, data readiness, risk, and enterprise supportability. - Ensure AI-enabled solutions are grounded in trusted enterprise data and designed for measurable business outcomes. - Help establish reusable AI design patterns, delivery approaches, and guardrails that accelerate future implementations, while staying closely engaged in critical AI architecture decisions and implementation trade-offs. 5. AI Delivery, Evaluation & Operational Readiness - Define and promote practical delivery patterns for AI solutions from experimentation through pilot, production rollout, monitoring, and continuous improvement. - Establish evaluation approaches for AI solution quality, groundedness, usefulness, reliability, and user adoption. - Partner with platform and engineering teams on AI operational readiness, including observability, feedback loops, versioning, change control, and cost