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

  • Equivalent professional experience 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

• At Schneider, we believe that every employee is a talent who deserves equal opportunities. This means you matter. Every individual needs to feel valued, supported, and treated fairly to do their best work. Our Total Rewards is our way of saying: “We see you. We value you”. It’s more than just pay and benefits- it’s a meaningful investment in you. It is designed for you to perform, grow, feel safe, and elevate your potential to shine as an impact maker.

  The expected pay range is USD 142,400 - USD 213,600 per year. This pay range includes base pay and short-term incentives. The compensation range for this full-time position applies to candidates located within the United States.