IT Manager - AI Platform Engineering & Data Science

Ferguson is hiring an IT Manager to lead platform engineering and data science capabilities that support enterprise AI, machine learning, and software delivery. The role oversees platform strategy, cloud architecture, DevSecOps, MLOps, and a technical team, partnering across the organization to deliver secure, scalable solutions.

Responsibilities:

• Lead, coach, and develop a high-performing team of platform engineers, architects, and technical specialists
• Define and execute the platform engineering, cloud, and data science roadmap in support of Ferguson's AI and technology strategy
• Design, build, and optimize scalable, secure, and reliable platform capabilities across Google Cloud Platform (GCP) and hybrid-cloud environments
• Partner with AI Engineering, Data Science, Product, and Architecture teams to enable the development, deployment, and operation of AI and machine learning solutions
• Drive DevSecOps, CI/CD, infrastructure automation, and platform reliability standards to improve delivery speed, quality, and operational efficiency
• Own and enhance MLOps and data science platform capabilities, supporting model development, training, deployment, and monitoring at scale
• Establish engineering standards, architecture patterns, API strategies, and reusable platform services that accelerate software delivery
• Lead platform modernization initiatives, including cloud adoption, developer experience improvements, automation, and legacy technology retirement
• Manage project delivery, budgets, vendor relationships, and team capacity to ensure successful execution of critical initiatives
• Supervise platform performance, security, availability, and scalability while proactively identifying and mitigating risks
• Build strong partnerships with business and technology leaders to align platform investments with organizational priorities
• Stay ahead of emerging cloud, AI, data, and engineering technologies to drive innovation and continuous improvement

Requirements:

• Must have at least 5 years of hands-on experience in platform engineering
• Experience in machine learning implementation and data analytics enablement is required
• You should have broad knowledge of how platform capabilities support integration and innovation across different business domains
• Must have prior experience directly leading engineering or technical talent, including performance management and career development for direct reports
• Must have direct experience in software programming with Java and/or Python, along with Software Architecture and Engineering expertise
• Must have experience in secure software delivery (DevSecOps) and continuous integration/continuous deployment (CI/CD), and pipeline development
• Must have hands-on experience architecting and operating platform services on Google Cloud Platform (GCP) as the primary hyperscaler, with working knowledge of Azure to support hybrid-cloud workloads and legacy system integration
• Must have experience creating operational dashboards, telemetry configurations and alerting templates for end-to-end flow of data services using APM and Data Logging solutions such as AppDynamics, DataDog etc
• Solid understanding and experience implementing software design patterns and modern standards
• Must be a hands-on software engineer able to work alongside a cross-functional team of software engineers, software architects, data scientists, and software quality engineers as needed
• Demonstrates high aptitude, initiative, and self-drive — proactively finds opportunities to improve the platform and data science tooling rather than waiting for direction, and leads technical work hands-on rather than purely delegating
• Strong leadership and interpersonal relationship building skills
• Strong written communication skills with the ability to deliver compelling presentations
• Bachelor's degree in information technology, computer science or related field
• equivalent experience
• experience with offshore/onsite consultants
• Experience enabling data science and ML workflows using cloud-native tooling (e.g., Vertex AI, BigQuery, or equivalent feature-store, pipeline-orchestration, and model-serving constructs)