Sr. Staff Engineer - Integration Engineering

At Penguin Solutions (Nasdaq: PENG) – The AI Factory Platform Company – we’re building a team of innovators who thrive on collaboration, creativity, and the opportunity to help shape the future of AI. As part of the AI technology revolution, our teams design, build, deploy, and manage AI factories for enterprises, sovereign AI initiatives, and neocloud providers worldwide.

Headquartered in Silicon Valley, California, Penguin Solutions operates globally through a network of R&D, manufacturing, and sales locations. For nearly three decades, we have operated at the intersection of memory and AI/HPC infrastructure. That engineering expertise positions us to power the next generation of AI workloads, from training to inference and agentic AI at scale.

Penguin Solutions brings together differentiated infrastructure software, advanced memory, compute systems, end-to-end services, and industry-leading partner solutions in a full-stack AI factory platform designed to help customers deploy and scale AI workloads with speed and precision.

At Penguin Solutions, we value ideas over hierarchy and believe in servant leadership, where leaders enable teams to do their best work. We empower employees to take ownership, drive innovation, and grow through challenging work, continuous learning, and exposure to advanced AI tools and technologies. With flexibility where it matters and a strong focus on outcomes, Penguin Solutions is a place to do your best work, grow your career, and make a meaningful impact.

Job Overview

The Integration Engineering team develops and validates the software solutions that power next-generation AI infrastructure. We work at the intersection of infrastructure, automation, AI platforms, and enterprise software to help customers deploy, operate, and scale AI environments with confidence.

As a Senior Staff Engineer – Integration Engineering, you will serve as a senior technical contributor, driving the architecture, development, integration, and validation of complex AI infrastructure solutions across Penguin's AI Factory Platform.

You will work across a broad ecosystem of technologies, including Linux, Kubernetes, HPC, networking, storage, GPUs, AI frameworks, and cloud-native infrastructure, to build scalable, reliable, and production-ready integrated solutions.

This is a hands-on technical leadership role requiring strong systems engineering expertise, software development and automation skills, architectural judgment, and the ability to influence engineering direction across teams.

You will collaborate closely with Product Management, Software and Hardware Engineering, Solution Architects, and technology partners to translate complex integration requirements into reusable engineering solutions.

Responsibilities

Architecture and Technical Leadership

• Lead the technical design and architecture of complex software and infrastructure integrations across Penguin's AI Factory Platform.
• Define scalable, maintainable, and reusable integration architectures spanning compute, networking, storage, orchestration, and AI platforms.
• Drive technical evaluations, architectural trade-offs, and proof-of-concepts for emerging technologies.
• Establish engineering standards, design patterns, and best practices for integration development.
• Provide technical guidance and mentorship to engineers while remaining actively involved in implementation.

Develop and Integrate AI Infrastructure Solutions

• Design, develop, and validate software integrations between Penguin products and third-party technologies.
• Build integration components, APIs, plugins, and automation frameworks that enable seamless interoperability.
• Integrate infrastructure orchestration platforms with Kubernetes, HPC schedulers, networking, storage, and GPU infrastructure.
• Drive end-to-end integration validation, including functional, performance, scalability, and reliability testing.
• Collaborate with Product Engineering to identify integration gaps and contribute improvements to platform capabilities.

Drive Automation and Engineering Efficiency

• Develop production-quality automation using Python, Ansible, Helm, Kubernetes, and CI/CD frameworks.
• Build reusable tools, workflows, and integration frameworks to automate infrastructure provisioning, configuration, validation, and lifecycle management.
• Establish automated integration testing and continuous validation practices.
• Leverage AI-assisted development tools such as Cursor, GitHub Copilot, Claude Code, and ChatGPT to improve engineering productivity and software quality.
• Promote engineering practices that improve reliability, repeatability, and maintainability.

Solve Complex Systems and Integration Challenges

• Lead root-cause analysis and resolution of complex issues spanning distributed systems, Linux, Kubernetes, networking, storage, GPUs, and AI infrastructure.
• Diagnose interoperability, performance, and scalability issues across multiple software and hardware platforms.
• Partner with internal engineering teams and external technology vendors to resolve integration challenges.
• Identify architectural improvements that enhance system reliability, operational efficiency, and customer experience.

Innovation and Cross-Functional Collaboration

• Evaluate emerging technologies across AI infrastructure, cloud-native platforms, automation, and agentic AI.
• Influence technical roadmaps by identifying opportunities for new integrations and reusable platform capabilities.
• Collaborate with Product Management, Engineering, and technology partners to translate customer requirements into technical solutions.
• Lead technical discussions, architecture reviews, and engineering initiatives across organizational boundaries.
• Mentor engineers and promote a culture of technical excellence, innovation, and continuous learning.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience.
• 10+ years of experience in software engineering, systems engineering, platform engineering, or infrastructure integration, with significant hands-on technical experience.
• Deep expertise in Linux-based distributed systems, including architecture, deployment, troubleshooting, and production operations.
• Strong experience with Kubernetes architecture, cluster lifecycle management, networking, and operations.
• Proven experience designing and developing software integrations, APIs, automation frameworks, and infrastructure tooling.
• Advanced programming and scripting skills in Python, Bash, or similar languages, with a strong emphasis on production-quality code.
• Strong expertise in Infrastructure as Code and automation frameworks, preferably Ansible, Helm, and Terraform.
• Experience with Git, CI/CD pipelines, automated testing, and modern software engineering practices.
• Demonstrated experience using AI-assisted development tools to improve engineering productivity and efficiency.
• Strong understanding of distributed systems, networking, storage, and infrastructure orchestration.
• Proven ability to lead complex technical initiatives, make architectural decisions, and influence engineering teams without direct management authority.
• Excellent analytical, troubleshooting, communication, and cross-functional collaboration skills.

Preferred Qualifications

Experience in one or more of the following areas:

• AI Infrastructure: GPU computing, NVIDIA or AMD GPU platforms, AI/ML infrastructure, and GPU lifecycle management.
• HPC and Workload Orchestration: Slurm, Kubernetes, GPU scheduling, and distributed workload management.
• Infrastructure Platforms: Bare-metal provisioning, infrastructure lifecycle management, and cloud-native orchestration.
• Networking: Ethernet, InfiniBand, SDN, network automation, and multi-tenant networking.
• Storage: Enterprise storage, distributed filesystems, and storage integration.
• Cloud-Native Technologies: Containers, Kubernetes operators, Helm, and platform engineering.
• Observability: Metrics, logging, monitoring, telemetry, and infrastructure health management.
• AI and Agentic Systems: AI agents, MCP, A2A, and integration with AI-driven infrastructure operations.
• DevOps and Automation: Infrastructure as Code, CI/CD, automated validation, and GitOps practices.
• Technology Ecosystem Integration: Experience integrating commercial and open-source infrastructure platforms.

What Success Looks Like

• Deliver scalable, production-ready integrations across Penguin's AI Factory Platform and partner ecosystem.
• Establish reusable integration frameworks and automation that accelerate customer deployments.
• Reduce integration complexity through strong architecture, engineering standards, and automated validation.
• Provide technical leadership across complex, cross-functional engineering initiatives.
• Help shape the evolution of Penguin's AI infrastructure software capabilities through hands-on innovation and engineering execution.

Location

Hybrid – Bangalore, India