AI Platform and Agentic Engineer

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Data Science, or a related field. 2.
• Significant experience in software engineering, AI engineering, platform engineering, or solution architecture. 3.
• Advanced Python development skills and experience building production APIs and backend services. 4.
• Demonstrated proficiency with Agentic AI coding assistants such as Claude Code, or comparable agentic software-development platforms. 5.
• Experience developing LLM applications, AI agents, tool-calling systems, retrieval pipelines, and structured model outputs. 6.
• Strong knowledge of software architecture, data modeling, design patterns, automated testing, version control, and code review. 7.
• Experience with CI/CD, logging, monitoring, secrets management, access control, and production operations. 8.
• Ability to design secure, reliable, scalable, and maintainable enterprise systems. 9.
• Strong written and verbal communication skills, including the ability to collaborate with multidisciplinary engineering teams.
• Preferred Qualifications 1.
• Master's degree in a relevant engineering or computer science discipline. 2.
• Experience in semiconductor design, silicon validation, product qualification, Design Quality and Reliability, failure analysis, or manufacturing. 3.
• Familiarity with reliability topics such as device aging, electromigration, ESD, latch-up, TDDB, soft-error reliability, and high-voltage qualification. 4.
• Experience with statistical analysis, engineering data pipelines, scientific visualization, and large engineering datasets. 5.
• Experience integrating enterprise databases, document repositories, laboratory systems, and quality-management platforms. 6.
• Familiarity with containers, cloud or on-premises platforms, relational databases, vector search, caching, and asynchronous processing. 7.
• Experience with AI evaluation, prompt testing, model observability, governance, and responsible-AI controls. 8.
• Experience building systems that require traceability, reproducibility, audit readiness, and protection of confidential intellectual property.
• What Success Looks Like The successful candidate will establish a trusted AI foundation for Intel's Design Quality and Reliability organization-not a collection of isolated prototypes.
• The platform will securely scale across engineering use cases, deliver explainable and traceable results, preserve appropriate human oversight, and measurably accelerate Intel's mission-critical quality and reliability work.