AI Solutions Engineer

About the role

Millions of people come to Pinterest to find creative ideas, dream about new possibilities, and plan for memories that will last a lifetime. Join us to help bring everyone the inspiration to create a life they love.

Responsibilities

• Discover and scope AI opportunities: Partner with internal teams across corporate functions to understand their workflows, pain points, and goals, and identify high-value AI/automation opportunities.
• Map and improve business processes: Document current workflows, identify bottlenecks, and propose AI-enabled changes that deliver clear business outcomes.
• Design end-to-end AI solutions: Design and implement AI-enabled tools and workflows that integrate with existing systems and data sources, and that are intuitive for non-technical users.
• Build and ship production-quality software: Write clean, maintainable code and tests. Use standard CI/CD and environment practices. Implement logging, monitoring, and basic guardrails to ensure performance, quality, cost, and reliability.
• Pilot, rollout, and drive adoption: Pilot, roll out, and drive adoption of solutions by working closely with end-users, gathering feedback, and iterating based on real-world usage.
• Champion for responsible AI: Ensure solutions follow privacy, security, and compliance expectations, especially when working with sensitive or regulated data.
• Build for reuse: Create and share reusable patterns, components, and documentation to make future AI/automation work faster and more consistent across teams.
• Accelerate Workflows with Generative AI and Automation: Leverage AI to accelerate execution, explore alternative solutions using AI, synthesize information with AI, and automate repeatable work.

Requirements

• Software engineering foundation. A CS, Engineering, Data Science, or related degree (or equivalent experience), with demonstrated ability to build and operate production systems — backend services, internal tools, integrations, or data applications.
• Hands-on AI and automation delivery. You've shipped AI-powered or automation-driven solutions in a real environment.
• Agentic AI literacy. You understand how modern agentic systems are constructed.
• System design and architecture thinking. You can sketch a data flow, reason about integration points, evaluate trade-offs between approaches, and design for failure — including fallbacks, retry logic, timeouts, and human escalation paths.
• Data and security judgment. You understand data access controls, the risks of giving AI broad access to sensitive information, PII minimization, audit logging, and what responsible data handling looks like in an enterprise environment.
• Business function acumen. You can engage credibly with stakeholders in Marketing, Finance, Sales, HR, Legal, or Operations — understanding their workflows, KPIs, and constraints well enough to scope solutions that fit their real needs.
• Clear, collaborative communication. You can explain architecture trade-offs to a Finance Manager and debug a prompt failure with an engineer in the same afternoon.

Qualifications

• Bachelor’s/Master’s degree in a relevant field such as Computer Science or equivalent experience.
• Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs.
• Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review).
• High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables.