AI Platform & Infra Architect

Job Summary

Responsible for assessing, defining, and championing the IT architecture strategies, principles, policies, and standards in alignment with Cummins IT strategy and governing their use. The AI Platform & Infra Architect coordinates with project teams to create, document, assess, and/or validate application and infrastructure architecture artifacts/designs for specific systems to meet Cummins security policies, governance, compliance, and regulations, and reviews these artifacts with stakeholders.

The AI Lab at Cummins develops robust, scalable infrastructure powering LLMs, agent frameworks, retrieval systems, knowledge platforms, and enterprise AI applications across Cummins’ global footprint. This role will design the backbone of Cummins’ AI ecosystem.

Key Responsibilities

• Applies enterprise architecture standards including styles, stacks, and patterns effectively.
• Develops project solution designs to meet business and technical requirements.
• Defines reference architectures aligned with IT technology standards for reuse.
• Participates as a change agent, driving strategic IT architecture direction (Cloud first, API first, DevOps, Agile).
• Leads technical analysis, solution design, and technology domain sessions to achieve optimum IT solutions.
• Builds strong relationships with customers and peers across IT to promote solution development using standard technologies.
• Maintains awareness of emerging technologies, software trends, and tools, recommending solutions based on cost/benefit analysis.
• Facilitates and supports pilot and proof of concept activities to validate technology capabilities.
• Collaborates with project teams to understand requested capabilities and recommends appropriate technologies.
• Provides infrastructure capacity recommendations for new applications and systems.

Role Overview

This senior technical role requires expertise across cloud architecture, distributed systems, LLM deployment, GPU infrastructure, networking, storage, and end-to-end engineering. You will architect the platforms that power Cummins’ AI solutions globally.

AI Infrastructure & Deployment Architecture

• Design scalable LLM inference and service architectures (vLLM, Triton, DeepSpeed-Inference).
• Architect hybrid cloud/on-prem compute for model serving, retrieval, and agent workflows.
• Ensure high availability, security, performance, observability, and compliance.

End-to-End Platform Engineering

• Build CI/CD, LLMOps, automation, and deployment pipelines.
• Optimize compute, networking (RoCE/IB), caching, and I/O across AI workloads.
• Implement monitoring, logging, tracing, and cost optimization.

Foundational Platform Capability Development

• Build platform components including data ingestion layers, vector databases, model serving gateways, and knowledge systems.
• Enable product teams and AI teams to develop scalable solutions on top of the infra.

Technical Leadership & Collaboration

• Partner with AI scientists, Solution Architects, and Product teams to ensure platform alignment.
• Lead technical design reviews and drive architectural standards.

Must-Have Qualifications

• Master’s degree in Computer Science, Distributed Systems, Cloud Computing, or related fields; OR equivalent industry experience.
• Strong hands-on experience with cloud-native systems: Kubernetes, Docker, microservices, service mesh.
• Deep expertise in GPU/TPU accelerators, distributed inference/training, high-performance networking.
• Proven experience deploying AI systems into production environments.
• Fluency across IaaS/PaaS/SaaS layers and hybrid cloud infrastructure.
• Strong backend and systems engineering ability (APIs, infra automation, DevOps/LLMOps).
• Experience in enterprise security and compliance.

Preferred Qualifications

• Experience in major cloud providers or AI infrastructure teams.
• Familiarity with industrial IT/OT integration.
• Experience architecting large-scale, high-performance inference clusters.

What You Will Gain

• Ownership of core AI infrastructure for a global enterprise.
• Opportunity to architect high-impact, scalable AI systems that support key engineering and industrial workflows.

Key skills/competency

• AI Architecture
• Cloud Computing
• Distributed Systems
• LLM Deployment
• GPU Infrastructure
• Kubernetes
• DevOps/LLMOps
• High-Performance Networking
• Enterprise Security
• Solution Design