AI Software Engineer

Who you are

• Backend & Distributed Systems Expertise: Strong engineering proficiency in Python, RESTful microservices, and deploying cloud-native applications within AWS environments (including containerized architectures)
• Application Security Mastery: Proven skill in threat modeling, secure API design, cryptography fundamentals, data protection, secrets management, and applying OWASP/supply-chain risk mitigations
• AI & Machine Learning Capabilities: Hands-on experience developing LLM-driven or Agentic AI applications, RAG pipelines, prompt engineering strategies, and production guardrails or model evaluation frameworks
• We are primarily an in-office environment and therefore, you will be expected to work from the Santa Clara office in compliance with Pure’s policies, unless you are on PTO, or work travel, or other approved leave

What the job involves

• As a Security-Focused AI Software Engineer on our Digital Product and AI Platform team, you will champion secure-by-design principles to safeguard production backend microservices and cutting-edge AI architectures
• Collaborating closely with engineering, product management, cloud infrastructure, and security teams, you will architect, build, and operate resilient systems that power enterprise AI capabilities
• By embedding automated security controls, threat modeling, and proactive vulnerability management directly into the development lifecycle, you will ensure our digital platform remains performant, trustworthy, and protected at scale
• Drive Application Security & Secure SDLC: Champion end-to-end security practices across the engineering team by conducting threat modeling, secure design reviews, and vulnerability remediations, ensuring all digital products and APIs adhere to least-privilege, robust authentication, and strict data protection standards
• Architect Secure AI & Microservices: Design, build, and deploy production-grade backend services and Agentic AI workflows in Python on AWS, integrating LLMs, retrieval-augmented generation (RAG) pipelines, and microservices with enterprise platforms
• Establish AI Guardrails & Governance: Implement safeguards—including output validation, automated prompt testing, fallback mechanisms, rate limiting, and human-in-the-loop workflows—to ensure reliable, deterministic, and safe AI behavior in production environments
• Elevate System Observability & Incident Readiness: Build monitoring, audit logging, and automated evaluation frameworks to proactively identify security signals, supporting rapid incident response and driving continuously improved engineering practices

The application process

• There is no application deadline and we accept applications on an ongoing basis until the job is filled