AI Integration Leader

About the role

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.

Key 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

• 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

Skills & Competency

• Technical Skills:

  • 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

  • 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

  • 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

• Competencies & Domain Expertise:

  • Technical Leadership

  • Solution Ownership: 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

  • 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

  • Autonomy & Growth

  • 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

Candidate Requirements

• Background:

  • 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

• Education:

  • Master's degree or PhD in Computer Science, Electrical Engineering, or equivalent

  • Educational equivalency considered

• Location:

  • Boston, MA

• Preferred / Remote Possible:

  • Languages:

  • Full professional proficiency in English is required

  • French language skills are considered a strong asset

• Travel:

  • ~20% - team engagements, project kick-offs, and occasional client-facing technical sessions