AI Integration Leader

Schneider Electric•Nashville, TN

4d•Hybrid

About The Position

Joining System.AI means becoming part of Schneider Electric’s global Services Center of Excellence at a pivotal moment of transformation, where AI, advanced analytics, and Condition-Based Maintenance (CBM) are redefining how critical infrastructure is operated, maintained, and optimized. System.AI is currently driving innovation in data center environments, one of the most demanding and fastest-growing segments, while actively expanding into industrial, healthcare, energy distribution, and microgrid ecosystems. This expansion creates a unique opportunity to shape how scalable, data-driven services are deployed across industries. As an AI Integration Leader, you play a central role across the full solution lifecycle — from qualified opportunity and tendering, through execution, to industrialization and scale. You ensure that solutions are not only technically sound, but also deliver measurable business value and can be replicated efficiently across customers and geographies. You act as the critical bridge between presales, architecture, and delivery, bringing execution realism into opportunity shaping and ensuring seamless continuity into deployment. By aligning customer needs, technical design, and operational constraints, you contribute directly to increasing win rates, reducing delivery risk, and accelerating time-to-value. This role combines deep technical leadership, customer engagement, and a scalable delivery mindset, positioning you as a key contributor to the growth, differentiation, and long-term impact of the System.AI practice.

Requirements

7+ years in technical delivery, systems integration, or applications engineering role, with direct experience in IIoT, cloud platforms, and industrial automation in data center or critical infrastructure environments.
Demonstrated ability to lead technical delivery in client-facing or cross-functional engagements.
IIoT Architecture: Sensors, gateways, edge computing, OPC-UA, MQTT, Modbus, and northbound data transmission. Deep understanding of data flow from asset to cloud in data center environments.
OT/IT Integration: Proven ability to design and implement integrations between OT systems (BMS, DCIM, SCADA) and cloud platforms — including protocol bridging, data normalization, and security boundary management.
Cloud Technologies: Familiarity with Azure services (Azure Container Apps, Blob Storage, Identity, IoT Hub …) Hands-on experience with cloud-native patterns: Kubernetes, containers, microservices, REST APIs, and CI/CD pipelines.
CBM & Predictive Maintenance: Condition Based Maintenance (CBM) monitoring principles, asset health modelling, failure mode analysis, and maintenance strategy frameworks. Able to translate analytics requirements into data collection and processing architectures.
Solution Development & Delivery: Full-lifecycle technical delivery: architecture, development, integration testing, validation, and deployment.
Cybersecurity & Compliance: Zero Trust principles, network segmentation, cloud security, access control, and security/compliance frameworks (IEC-62443, NIS2, ISO 27001, GDPR) as applied to data center and cloud deployments.
Technical Leadership: Holds the technical line from design through delivery, ensuring implementation decisions remain consistent with agreed architecture and customer constraints.
System Thinking: Designs and evaluates solutions with downstream dependencies, failure modes, and long-term operability in mind.
Problem-Solving: Proactive in surfacing and resolving technical blockers; comfortable navigating fast changing environment.
Client-Centric Mindset: Builds deep understanding of customer environments and operational goals; ensures technical decisions serve the customer's real-world needs.
Team-Oriented: Works collaboratively across cross-functional teams, fostering a positive, inclusive team environment. Drives collaborative engineering culture by open knowledge sharing and engaging in constructive retrospectives.
Structured Communication: Expresses technical concepts clearly in written and verbal form, adapting style for engineering teams, customer technologists, and business stakeholders alike.
Cultural Awareness and Sensitivity: Recognizing and respecting the diverse backgrounds and perspectives of colleagues and clients, especially when working on international or multicultural projects.
Self-Direction: Identifies a path forward on ambiguous delivery challenges and executes without requiring full specification.
Adaptability: Adjusts to evolving customer requirements, changing project scopes, and emerging technical constraints without loss of momentum.
Continuous Improvement: Actively contributes learnings from pilot delivery back into reusable frameworks, tooling improvements, and internal best practices.
Full professional proficiency in English is required.

Nice To Haves

AVEVA Technologies: Hands-on experience with AVEVA CONNECT for industrial data ingestion, contextualization, and cloud-based analytics delivery. Certifications on AVEVA CONNECT and Edge Data Store are strong assets.
French language skills are considered a strong asset.

Responsibilities

Own the full technical lifecycle of System.AI solutions: qualified opportunity → tendering → design validation → build → integration → deployment → handover
Ensure consistency between architecture, execution, and customer constraints across all phases
Act as the single point of technical accountability of data and AI solution for pilots and ETO solutions
Drive the transition from pilot to scalable solution, contributing to reusable architectures, standardization, and deployment frameworks enabling replication across customers and regions
Engage in qualified opportunities, partnering with Sales to shape executable and competitive offers
Lead technical qualification, feasibility validation, and risk assessment prior to tendering
Contribute to solution definition, data and AI architecture choices, scope clarification, and effort estimation
Support technical proposal creation, customer presentations, and bid defenses
Ensure alignment between proposed data/AI solutions and the overall scope, minimizing execution risk
Own the design and delivery of the end-to-end data and AI solution, from data acquisition to analytics and value generation
Ensure data pipelines are reliable, secure, and fit-for-purpose for predictive maintenance and AI use cases
Validate that AI models and agentic capabilities are deployable, scalable, and aligned with operational constraints
Ensure the data & AI solution delivers measurable business outcomes, validating performance of predictive models, accuracy of insights, and impact on maintenance and operations KPIs
Ensure data quality, contextualization, and governance standards are consistently applied
Bridge the gap between data engineering, AI/analytics, and operational deployment
Lead day-to-day technical execution, ensuring alignment between architecture, implementation, and customer expectations
Coordinate engineering, data, and integration teams, driving clarity on priorities, deliverables, and timelines
Ensure data & AI solution integrity, enforcing best practices across development, integration, testing, and data pipelines
Proactively manage technical risks, issues, and governance (architecture compliance, cybersecurity, quality)
Drive deployment readiness and handover, ensuring validated solutions, proper documentation, and operational continuity
Partner with project managers and business leaders to define technical timelines, resource requirements, and delivery milestones; flag risks that affect schedule or scope in a timely manner.
Interface with engineering and technical team members for issue escalation, capability gap resolution, and roadmap acceleration ensuring customer-facing blockers are addressed efficiently
Collaborate with the ETO Solutions Product Owner to feed implementation learnings back into reusable tooling, components, and internal frameworks that reduce future pilot delivery effort.
Lead the structured handover of completed pilots to country operations teams to ensure full transfer of technical documentation, architectural context, operational runbooks, and institutional knowledge required for the receiving team to execute and scale independently.