AI Solutions Engineer

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 Goal of Section: Convey impact of the business group they will be joining on Marvell and the world. Questions To Answer In This Section: • What can’t Marvell do if they do not have this team to execute? • What projects is this team responsible for delivering? What do they work on every day? • Why should you work on this team vs another team doing the same project at another company? • How is this technology used in the world that most people would recognize? What You Can Expect We are looking for a mid-level AI Solutions Engineer to design and deliver AI agents and assistants that solve real operational problems for internal business teams. This is a solutioning-first role: you'll spend as much time scoping the right approach, designing the integration, and choosing the right tool for the job as you will writing code. You'll take ambiguous, half-formed business asks and turn them into working, production-grade systems, then support them once they are live. Solutioning & Solution Design • Requirements-to-design translation - Work directly with business stakeholders to turn ambiguous problems into clear technical designs, identifying data sources, system boundaries, and integration points before a line of code is written. • Build-vs-configure decisions - Assess whether a use case is better served by a custom-built agent or by configuring a no-code/low-code agent on an enterprise agent-builder platform (e.g., Glean), and design accordingly so business teams can eventually own and extend it themselves. • Integration architecture - Design how a new agent will talk to existing systems, APIs (e.g., Graph API), scheduled jobs, data platforms, and chat surfaces (e.g., Slack), and plan migrations off legacy approaches (e.g., retiring RPA in favor of clean API-driven pipelines). • Access & data-layer design - Design access control and data permission models for new solutions (e.g., Databricks ACLs, Unity Catalog) so that mobile, web, and API consumers all get correctly scoped access from day one. • Solution validation - Prototype quickly, validate the approach against real data and real users, and iterate the design before committing to a full production build. • Stakeholder-facing solution presentation. Demo and walk stakeholders through the proposed solution and trade-offs, adjusting the design based on feedback, and building the case for adoption across teams or regions. Delivery & Operations Once a solution is designed, you'll also help build and run it: • Build backend integrations and automation (Python, REST/Graph APIs, cron-based scheduling) • Package backend logic as clean API endpoints for frontend teams to consume (e.g., Next.js/Node.js) • Integrate LLM observability and evaluation tooling (e.g., LangSmith) to monitor groundedness, quality, and cost in production • Prepare documentation and present at architecture/compliance review boards (ARB) ahead of releases • Provide ongoing support and iteration for live solutions based on user feedback What We're Looking For • 2–4 years' experience building applied AI/software solutions in a hands-on engineering role • Strong Python skills; comfortable designing and consuming REST/Graph APIs • Experience translating business requirements into technical solution designs, not just implementing specs handed to you • Working knowledge of LLM/agent frameworks and observability tooling (e.g., LangSmith, LangGraph, or comparable) • Familiarity with cloud infrastructure (AWS or equivalent) and data platforms with access-control models (Databricks, Unity Catalog, or similar) • Comfortable presenting solution designs and trade-offs to non-technical stakeholders • Experience with enterprise agent-builder or search platforms (e.g., Glean). LLM evaluation methodologies (groundedness, hallucination detection), experience with Slack API or other chat-platform integrations as well as prior work in a rapid-prototyping or forward-deployed engineering capacity will be great additions and good to haves. 
This role is eligible to participate in Marvell's bonus and equity plans, including a new hire equity grant and annual equity refreshers, ensuring you are rewarded for your contributions and remain invested in the company's long-term success.
 Additional Compensation and Benefit Elements With competitive compensation and great benefits, you will enjoy our workstyle within an environment of shared collaboration, transparency, and inclusivity. We’re dedicated to giving our people the tools and resources they need to succeed in doing work that matters, and to grow and develop with us. For additional information on what it’s like to work at Marvell, visit our Careers page. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Interview Integrity To support fair and authentic hiring practices, candidates are not permitted to use AI tools (such as transcription apps, real-time answer generators like ChatGPT or Copilot, or automated note-taking bots) during interviews. These tools must not be used to record, assist with, or enhance responses in any way. Our interviews are designed to evaluate your individual experience, thought process, and communication skills in real time. Use of AI tools without prior instruction fro…