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
We are seeking an experienced Senior Manager, Software Engineering to lead engineering teams building core capabilities of the ClusterWareAI platform, with strong exposure to Data Science, Machine Learning, Generative AI, and Agentic AI.
ClusterWareAI enables deployment, orchestration, monitoring, optimization, and lifecycle management of large-scale AI infrastructure. This role will lead engineering across distributed systems, Kubernetes, infrastructure orchestration, observability, and AI-enabled platform capabilities.
The ideal candidate is a strong engineering leader who can build high-performing teams, drive technical execution, and work closely with Data Science and AI teams to productionize intelligent capabilities.
As a Senior Engineering Manager, you will provide technical and organizational leadership, drive execution across multiple initiatives, develop engineering leaders, and help establish Bangalore as a key engineering center for ClusterWareAI.
Responsibilities
- Lead, grow, and develop high-performing software engineering teams responsible for key components and services within the ClusterWareAI platform.
- Drive roadmap execution across multiple engineering initiatives from architecture and design through development, validation, release, and production support.
- Partner with Product Management, Principal Engineers, and Architects to define technical strategy, architecture, and execution priorities aligned with product and customer requirements.
- Partner with Data Science and AI teams to productionize ML models, analytics, GenAI, and agentic capabilities.
- Establish engineering practices for AI/ML reliability, observability, security, evaluation, and governance.
- Provide technical leadership for large-scale distributed systems and cloud-native platform capabilities spanning infrastructure orchestration, cluster management, APIs, Kubernetes, and AI infrastructure.
- Guide architectural decisions and technical trade-offs related to scalability, availability, performance, security, maintainability, and operational complexity.
- Drive the development of reliable control-plane services and APIs for provisioning, orchestration, monitoring, and lifecycle management of large-scale AI infrastructure.
- Champion engineering excellence through strong software architecture, code quality, test automation, CI/CD, observability, security, resiliency, and operational practices.
- Build platforms designed for large-scale and multi-tenant environments, with strong consideration for isolation, resource governance, high availability, failure recovery, and non-disruptive lifecycle operations.
- Establish measurable engineering and operational goals covering delivery predictability, product quality, reliability, performance, and engineering productivity.
- Partner with Quality Engineering, Support, and customer-facing teams to resolve complex field issues, drive root-cause analysis, and translate customer learnings into product and engineering improvements.
- Recruit, mentor, and retain strong engineering talent while developing senior engineers and technical leads into broader technical and organizational leadership roles.
- Create an environment of ownership and accountability where teams can make effective technical decisions and deliver customer outcomes with appropriate autonomy.
- Champion AI-assisted software development practices across the engineering organization, including the effective use of AI coding assistants and agentic development tools for design, implementation, testing, code review, debugging, documentation, and developer productivity, while establishing appropriate quality, security, and governance practices.
- Drive continuous improvement in developer experience and engineering productivity through modern development environments, automation, AI-assisted workflows, and measurable engineering practices.
- Identify dependencies and risks across engineering teams and drive alignment and resolution across organizational boundaries.
- Communicate engineering strategy, execution status, technical trade-offs, risks, and outcomes effectively to senior engineering and business leadership.
- Collaborate closely with globally distributed teams while continuing to build ClusterWareAI engineering capability and technical expertise in Bangalore.
- Evaluate and adopt AI-assisted software development approaches that improve engineering productivity while maintaining appropriate architecture, review, security, and quality standards.
Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
- 15+ years of software engineering experience building enterprise software, distributed systems, cloud platforms, or infrastructure software with Practical exposure to Data Science, ML, GenAI, or AI/ML platforms.
- 5+ years of engineering leadership experience, with a demonstrated ability to lead and develop high-performing software engineering teams.
- Experience leading multiple engineering initiatives, teams, or technical workstreams for a complex commercial software product or platform.
- Understanding of LLMs, RAG, AI agents, model serving, evaluation, and ML lifecycle.
- Ability to work effectively with Data Scientists and ML/AI Engineers.
- Experience using AI-assisted development tools and workflows.
- Strong technical depth in distributed systems, cloud-native architectures, microservices, APIs, and infrastructure software.
- Strong understanding of Kubernetes, containers, Linux, REST/gRPC APIs, and modern service architectures.
- Experience designing and delivering highly available, scalable, resilient, and secure production software.
- Strong understanding of modern software engineering practices including architecture and design reviews, Agile development, automated testing, CI/CD, DevOps, observability, and production operations.
- Ability to engage deeply in architecture and technical design discussions and provide effective guidance on complex engineering decisions.
- Experience owning software through the complete lifecycle—from requirements and architecture through development, deployment, operation, and continuous improvement.
- Demonstrated ability to recruit, coach, mentor, and develop senior engineers and technical leaders.
- Demonstrated experience adopting AI-assisted software development tools and workflows, with an understanding of how to apply them effectively while maintaining code quality, security, architectural integrity, and engineering accountability.
- Strong customer and product mindset with the ability to translate customer requirements and operational challenges into engineering priorities.
- Experience collaborating effectively across Product Management, Architecture, Quality Engineering, Support, and other engineering organizations.
- Excellent communication, collaboration, and stakeholder-management skills, including experience working with globally distributed teams.
Preferred Qualifications
Experience with one or more of the following:
- AI infrastructure, GPU cloud/neocloud platforms, or large-scale cloud infrastructure.
- Building cloud control planes, infrastructure management platforms, or orchestration systems.
- Kubernetes platform engineering and large-scale container orchestration.
- GPU infrastructure, heterogeneous accelerator environments, or GPU resource management.
- Bare-metal provisioning, fleet management, and infrastructure lifecycle automation.
- Multi-tenant platforms, resource scheduling, quotas, isolation, and infrastructure security.
- High-performance networking technologies including InfiniBand, RDMA/RoCE, and high-speed Ethernet.
- Distributed storage and data infrastructure.
- Infrastructure automation and Infrastructure as Code technologies such as Ansible.
- Observability, Site Reliability Engineering practices, service-level objectives, high availability, and automated remediation.
- Workload management and scheduling technologies such as Kubernetes and Slurm.
- Hybrid cloud and enterprise SaaS platforms.
- High Performance Computing environments.
- AI/ML platform infrastructure, LLM infrastructure, or AI-enabled infrastructure operations.
- Building or scaling engineering teams within a growing product or engineering organization.
Location
Hybrid – Bangalore, India
Travel
Less than 10%