Principal Full Stack Developer & Solutions Architect – AI, Data & Cloud

Job Description

Western Digital is seeking a Principal Full Stack Developer and Solutions Architect to design and build the AI, data analytics, and cloud solutions used by our Product Development engineering organization. This is a hands-on principal role: you will own the solution architecture, write the code, and design the interfaces engineers actually work in.

You will sit with product engineering teams, identify where AI, data, and cloud capability create real leverage, and take those use cases from prototype to production. Your scope spans the full solution — agentic and AI/ML services, the data and analytics layer beneath them, and the cloud platform they run on. You will set the design patterns, evaluation standards, and quality gates that the wider team builds against, and act as the senior technical counterpart to the AI Center of Excellence, Enterprise Data, and Engineering Cloud organizations. Success in this role is measured by engineering productivity gained, not only by systems deployed.

Essential Duties and Responsibilities:

Solution Architecture & Technical Leadership

• Own end-to-end solution architecture for Product Development use cases spanning AI/ML, data analytics, and cloud — from data source through to the engineer’s interface.
• Establish reference architectures, design patterns, and technology standards, and select the tools the organization will standardize on.
• Make build-vs-buy decisions and defend them to IT and business leadership.
• Ensure solutions align with enterprise security and data governance requirements by design.
• Provide design review and technical mentorship to the AI/Data Engineer and Platform/DevOps Engineer on the team.
• Ability and proven track record managing software developers

AI & Agentic Systems

• Design agent systems for engineering workflows: tool use, orchestration, multi-agent boundaries, and human-in-the-loop controls.
• Define evaluation methodology and quality gates — golden sets, regression suites, LLM-as-judge — so that no solution reaches an engineer’s desk unvalidated.
• Set model routing and guardrail policy through the enterprise LLM gateway.
• Determine where AI is the right answer and where a deterministic pipeline, analytics solution, or existing automation is the better engineering decision.

Data & Analytics

• Architect the data and retrieval layer that AI and analytics solutions depend on: ingestion, modeling, semantic layers, and access patterns across engineering and test data.
• Select and integrate data analytics and BI tooling, and enable self-service analytics for engineering teams rather than one-off reporting.
• Design pipelines and data products that land on Enterprise Data foundations rather than duplicating them.
• Ensure data quality, lineage, and permission propagation so that downstream AI and analytics outputs are trustworthy.

Cloud & Platform

• Architect solutions for AWS and containerized runtimes, balancing performance, reliability, and cost.
• Define deployment, infrastructure-as-code, and CI/CD patterns in partnership with the Platform/DevOps Engineer.
• Engineer for latency, inference cost, and cloud spend as first-class design constraints.
• Build on Engineering Cloud and AI CoE platform foundations rather than constructing parallel stacks.

Full Stack Development & User Experience

• Build applications end-to-end: backend services, orchestration logic, APIs, data access, and the user-facing surface.
• Own the user experience — how work is initiated, how progress is made visible, and how output is reviewed and corrected by the engineer.
• Decide embedded-versus-standalone delivery across Slack, Teams, IDE, BI, and web surfaces, and ship working pilot interfaces without waiting on a front-end team.
• Define the interaction patterns that ITES-US Engineering & Web Apps productionizes for scaled deployment.

Delivery & Enablement

• Partner directly with Product Development engineers to identify, scope, and co-develop solutions; work on-site alongside engineering teams on a regular cadence.
• Translate Product Development leadership priorities into a sequenced solution roadmap with measurable outcomes.
• Run skill-transfer sessions so that PD engineers can extend and maintain what is built.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Physics or a related field; advanced degree a plus.
• 8+ years of professional software engineering experience, including production ownership of systems you designed.
• Demonstrated solution architecture experience spanning more than one domain — application, data, cloud, or AI — with the ability to own a design end-to-end.
• Strong Python and SQL; proficiency in a modern front-end stack (React/TypeScript) or rapid UI frameworks (Streamlit, Gradio) sufficient to ship a usable internal tool independently.
• Demonstrated experience building LLM-based applications with orchestration frameworks (LangGraph, Langflow, or equivalent), including tool/function calling and MCP-based integrations, and practical experience with RAG architectures.
• Hands-on experience with data engineering and analytics tooling — pipeline orchestration (Airflow, dbt, or equivalent), warehouse or lakehouse platforms, and BI or visualization tools.
• Solid AWS experience, including containerized workloads (Kubernetes/EKS), infrastructure-as-code, and cost-aware architecture.
• Working knowledge of evaluation methodology for non-deterministic systems, with a track record of measuring quality rather than asserting it.
• Excellent written and verbal communication; able to present technical direction to director and VP audiences.
• Ability to work on-site in San Jose, CA, with regular embedded time alongside engineering teams.

Preferred Qualifications

• Prior delivery of an AI or analytics solution in production at meaningful scale, with adoption evidence.
• Experience in semiconductor, storage, or hardware product development environments, including engineering and test data.
• Familiarity with engineering toolchains — GitHub, Jira, Confluence, CI/CD systems, and test data platforms.
• Design-system literacy and prior ownership of a developer-facing product surface.
• Experience operating within an enterprise AI platform (LLM gateway, RAG-as-a-service, managed inference) and enterprise data governance frameworks.

Key Competencies

• Architect who ships — comfortable moving between whiteboard and codebase in the same day
• Breadth across AI, data, and cloud, with the judgment to know which one the problem actually calls for
• Product sense: judges success by engineer adoption, not by technical elegance
• Ability to influence without authority across IT and engineering organizations
• Comfortable with ambiguity in a newly formed team
• Collaborative and customer-centric mindset toward the engineers being served

What Success Looks Like

• Use cases move from prototype to production adoption within Product Development, with measurable engineering time returned.
• Documented reference architectures and evaluation standards exist across AI, data, and cloud, and are used by the wider team.
• Product Development engineers request the team’s involvement rather than being recruited into it.
• Solutions are built on enterprise foundations, with no parallel stacks created.

Additional Information

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