AI Lab Infrastructure and Operations Manager

Job Summary:

- Provides system operational support ensuring the effectiveness, efficiency, accuracy, and availability of systems for end users. Coaches and mentors less experienced team members.

Key Responsibilities:

- Performs routine maintenance, facilitates customization and changes, and ensures system disaster recovery.

- Leads new employee technology setup, including computer setup, delivery, and system access.

- Resolves non-obvious issues and performs authorized changes and upgrades to existing systems.

- Performs preventative maintenance on IT systems and networks, and monitors system performance and accuracy.

- Conducts complex root-cause analysis to identify issues.

- Coaches and mentors less experienced security analysts, and documents knowledge and solutions for use across the organization

- Ensures IT services meet agreed performance, availability, and customer satisfaction standards by monitoring service metrics and proactively addressing issues impacting service quality.

- Collaborates with Service Delivery Leaders and Engineering teams to validate fixes, confirm resolution, and implement preventive measures to avoid future service disruptions.

AI Lab Infrastructure & Operations Manager

Cummins AI Lab - Artificial Intelligence Laboratory

About Cummins AI Lab

The Cummins AI Lab integrates applied AI research, engineering workflows, manufacturing processes, and enterprise platforms to deliver high-value AI solutions across Cummins' global operations. We build and operate the infrastructure, technology ecosystem, and experimentation environment that enable rapid AI innovation, proof-of-concept validation, and scalable enterprise AI adoption.

Role Overview

The AI Lab Infrastructure & Operations Manager owns the reliable, secure, and efficient operation of the AI Lab environment. The role manages software platforms, AI services, hardware assets, computing resources, lab infrastructure, vendors, contractors, and supporting team members. It ensures that researchers, solution architects, and project teams can access fit-for-purpose platforms, tools, environments, and operational support.

This is a management and technical enablement role. The emphasis is on technology evaluation, introduction, governance, resource optimization, service continuity, and operational excellence rather than deep architecture design, platform engineering, or framework development.

Key Responsibilities

1. Software Platform & AI Service Operations

- Manage AI cloud environments, AI platforms, hosted AI services, model APIs, and related service subscriptions used by the AI Lab.
- Coordinate the onboarding, configuration, operation, maintenance, and lifecycle management of AI services and platforms.
- Evaluate and introduce external foundation models, model inference services, deployment frameworks, and technical tools based on lab needs.
- Conduct fit-for-purpose technical assessments covering usability, compatibility, reliability, security, operational effort, scalability, and cost.
- Maintain service inventories, ownership records, operating guidance, access models, and support processes for lab software capabilities.
- Coordinate incident resolution, service changes, renewals, and escalation with internal IT teams and external providers.

2. Hardware Infrastructure & Computing Resource Management

- Manage AI all-in-one servers, AI mobile workstations, GPU computing resources, lab devices, and related infrastructure assets.
- Plan and coordinate resource allocation, workload scheduling, capacity usage, and computing availability across AI Lab activities.
- Monitor utilization, availability, maintenance status, and operational risks, and recommend actions to improve effective resource use.
- Own hardware inventory, assignment, maintenance coordination, upgrade planning, warranty tracking, and lifecycle management.
- Coordinate equipment installation, configuration, technical support, and issue resolution with vendors, contractors, and enterprise IT teams.

3. AI Lab Environment, Network & Operational Governance

- Lead day-to-day management and operations of the AI Lab infrastructure environment.
- Coordinate lab environment readiness, network connectivity, access, accounts, permissions, storage, and supporting services.
- Establish and maintain operating standards for resource requests, environment access, equipment usage, change management, incident handling, and asset governance.
- Maintain operational documentation, service runbooks, inventories, usage records, and management dashboards.
- Drive service continuity, issue prevention, operational risk management, and continuous improvement for lab infrastructure and services.

4. Data Security, Compliance & Risk Coordination

- Coordinate data security, cybersecurity, privacy, and compliance requirements for AI Lab platforms, models, devices, networks, and external services.
- Partner with enterprise IT, cybersecurity, architecture, procurement, legal, and relevant governance teams to complete required reviews and controls.
- Support access control, data classification, secure configuration, logging, patching, vulnerability handling, and risk remediation processes.
- Assess operational and security implications before introducing external models, cloud services, inference frameworks, or new devices.
- Maintain risk, issue, exception, and remediation tracking for the lab infrastructure environment.

5. Technology Evaluation & Lab Enablement

- Scan and evaluate emerging AI infrastructure technologies, AI services, model deployment options, inference frameworks, hardware solutions, and productivity tools.
- Organize trials and technical evaluations with solution architects, research scientists, users, contractors, and vendors.
- Provide practical recommendations on platform selection, service adoption, resource use, operational readiness, and future infrastructure needs.
- Enable PoC teams by provisioning suitable environments, coordinating computing resources, and resolving infrastructure dependencies.
- Capture evaluation findings, limitations, operating requirements, and recommendations for reuse across the AI Lab.

6. Vendor, Contractor & Budget Management

- Manage relationships and operational coordination with cloud providers, hardware suppliers, software vendors, service providers, and technical partners.
- Define work packages, assign tasks, track deliverables, review service quality, and manage performance for contractors supporting AI infrastructure and lab operations.
- Coordinate quotations, procurement inputs, contracts, renewals, licenses, warranties, and support arrangements with relevant internal teams.
- Monitor infrastructure and service spending, usage, commitments, and budget risks, and identify optimization opportunities.
- Lead issue escalation and resolution across vendors, contractors, internal IT teams, and AI Lab stakeholders.

7. Team Leadership & Operational Excellence

- Lead employees, contractors, and support resources responsible for infrastructure and operations activities.
- Set priorities, clarify ownership, allocate work, review outcomes, and build a reliable service-oriented operating culture.
- Develop team capability in platform operations, infrastructure management, lab governance, documentation, and user support.
- Define service expectations and track relevant availability, utilization, incident, delivery, and customer support indicators.
- Develop and execute an AI Lab infrastructure and operations roadmap aligned with lab growth and project demand.

Required Qualifications

- Bachelor's or Master's degree in Information Technology, Computer Science, Engineering, AI, or a related discipline, or equivalent practical experience.
- Experience in infrastructure operations, platform operations, technical operations, IT service management, lab operations, or a related field.
- Hands-on experience managing cloud platforms, enterprise technology services, servers, workstations, networks, or technical environments.
- Experience coordinating vendors and service providers and managing contractors or technical support resources.
- People leadership experience, including work planning, delegation, performance follow-up, capability development, and stakeholder management.
- Working knowledge of security, access control, data protection, asset management, incident management, and operational risk practices.
- Strong organizational, communication, documentation, problem-solving, and cross-functional coordination skills.
- Ability to balance multiple operational priorities in a fast-moving innovation environment.

Preferred Qualifications

- Experience supporting AI, data science, software engineering, digital innovation, research, or laboratory environments.
- Familiarity with generative AI platforms, foundation model services, model APIs, model deployment, and inference frameworks.
- Experience managing GPU servers, AI appliances, high-performance workstations, shared computing resources, or workload scheduling.
- Experience with cloud cost management, software licensing, procurement support, vendor performance, or service-level management.
- Experience working in engineering, manufacturing, industrial, or enterprise technology organizations.
- Understanding of the AI solution lifecycle from experimentation and PoC validation through production transition.

Success Measures

- Stable, secure, and well-governed operation of AI Lab platforms, services, devices, and environments.
- Effective utilization and transparent allocation of shared computing resources.
- Timely onboarding and objective evaluation of external models, services, frameworks, and tools.
- Clear asset, service, access, risk, and operational records.
- Reliable vendor and contractor delivery with effective cost and service management.
- Positive enablement of AI Lab teams and continuous improvement of operational maturity.

What You Will Gain

- Ownership of the operational foundation supporting AI innovation and experimentation.
- Exposure to a broad ecosystem of AI platforms, services, models, infrastructure, and emerging technologies.
- A leadership role connecting AI teams, enterprise IT, cybersecurity, procurement, vendors, and technical partners.
- Opportunity to shape scalable and sustainable operating practices for an enterprise AI Lab.