Principal Architect & Lead Engineer – Enterprise AI Platforms

About Marvell

Marvell’s semiconductor solutions are the essential building blocks of the data infrastructure that connects our world. Across enterprise, cloud and AI, and carrier architectures, our innovative technology is enabling new possibilities. 

At Marvell, you can affect the arc of individual lives, lift the trajectory of entire industries, and fuel the transformative potential of tomorrow. For those looking to make their mark on purposeful and enduring innovation, above and beyond fleeting trends, Marvell is a place to thrive, learn, and lead. 

Your Team, Your Impact

At Marvell, we’re accelerating AI adoption—not just in our products, but in how we operate. This is a rare opportunity to architect and lead a transformative function that embeds AI across every facet of our business.
We are building the next generation of agentic enterprise AI platforms — systems that enable the creation, deployment, and evolution of autonomous, goal-driven workflows, intelligent assistants, and domain-specific AI agents across the organization.

As our Platform Architect & Lead Engineer, you will play a pivotal role in designing and building the foundational architecture for multi-agent orchestration, distributed inference, and model lifecycle management — integrating data, models, tools, and event-driven workflows orchestration into a cohesive architecture that accelerates enterprise-wide AI adoption.

This is a hands-on technical leadership role, ideal for an architect-engineer who thrives in greenfield environments, can balance build vs. buy decisions, and enjoys working across technical and business boundaries.

What You Can Expect

Platform Architecture & Development

• Architect and lead development of core AI platform capabilities, including agent orchestration layers, memory stores, function calling frameworks, and evaluation pipelines for continuous improvement.
• Design scalable, microservices-based services supporting multi-agent collaboration, retrieval-augmented generation (RAG), tool and API chaining, message passing, and event-driven orchestration.
• Partner with the Data Office to integrate data pipelines, vector databases, feature stores, and model registries into cohesive vertical solutions.
• Evaluate, design, and implement model training, serving, and continuous improvement frameworks that connect seamlessly with enterprise systems.
• Own system architecture decisions reference implementations, and the platform roadmap, including build vs. buy assessments, model routing, and integration with third-party or open-source frameworks.
• Define observability, reliability and reliability baselines using Prometheus, OpenTelemetry, and Grafana to monitor agent performance, latency, and health.

Team Leadership & Collaboration

• Lead and mentor a small, high-impact team of engineers and contractors.
• Define engineering standards, API design patterns, and CI/CD pipelines for platform components.
• Collaborate with business, product, and IT leaders to align platform capabilities with strategic goals and use cases.
• Serve as a trusted technical advisor to the Head of Enterprise AI & Platform on architectural strategy, scalability and platform evolution.

AI Ecosystem & Thought Leadership

• Stay at the forefront of the evolving AI ecosystem — especially in agentic systems, distributed reasoning, and multi-model orchestration.
• Develop and maintain a clear point of view on architecture evolution, agent interoperability standards, and scaling patterns.
• Contribute to AI governance, model observability frameworks, prompt security and responsible AI design.

What We're Looking For

Preferred Experience

• 8–12+ years in software or platform engineering; 3+ years in architecture or technical lead capacity.
• Proven experience architecting enterprise-grade AI/ML or data platforms, ideally with components for workflow orchestration, service integration, or multi-agent coordination.
• Deep understanding of enterprise architecture, modular design, and security and identity frameworks (OAuth2, RBAC, SSO), and scalability across distributed environments.
• Hands-on experience with cloud-native technologies and modern engineering stacks (Python, Go, TypeScript, Terraform, Docker, Kubernetes, Kafka, Redis, etc.).
• Familiarity with LLM orchestration frameworks and vector databases or RAG pipeline architectures.
• Knowledge of feature engineering, dataset versioning, and evaluation frameworks (Feast, DVC, Hydra).
• Ability to balance rapid prototyping with long-term platform robustness and compliance requirements.
• Strong stakeholder management skills; able to communicate architectural decisions to both execut