Technical Lead, AI-Native Security Tools

Cisco Systems, Inc; San Jose (CA); Hybrid; USD 172,000 - 245,000; Full time

Job summary

Cisco Systems, Inc is seeking a Technical Lead for AI-Native Software Engineering (hybrid) within the Security Workflow Engineering team. You will set technical direction, guide engineers on architectural choices, and work across teams on cross-cutting problems.

This role combines leadership with hands-on coding to accelerate secure AI-enabled development. Candidates should have deep software engineering experience, a track record in architectural decisions, and fluency with AI-native practices.

Qualifications

• BS or MS degree in Computer Science, Computer Engineering, or related technical field, or demonstrated equivalent experience.
• Hands-on software engineering experience with architecture decisions that mattered.
• Deep expertise in design, implementation, and scaling of software systems.
• Experience mentoring engineers on technical decisions without management responsibilities.
• Fluency in AI-native software development practices.

Responsibilities

• Set technical direction for the team's portfolio.
• Lead on the hardest technical problems with defensible architecture.
• Guide engineers on architectural choices with well-reasoned judgment.
• Collaborate with other teams to drive shared decisions with urgency.
• Stay hands-on by reviewing designs and code where it matters.

Skills

Architectural decision making

Software engineering depth

Technical judgment

Stakeholder collaboration

Mentoring engineers

Education

BS or MS degree in Computer Science, Computer Engineering, or related field

Tools

APIs & tools for AI agents

MCP servers / function interfaces

Job description

Cisco Systems, Inc is seeking a Technical Lead for AI-Native Software Engineering (hybrid) within the Security Workflow Engineering team. You will set technical direction, guide engineers on architectural choices, and work across teams on cross-cutting problems.

This role combines leadership with hands-on coding to accelerate secure AI-enabled development. Candidates should have deep software engineering experience, a track record in architectural decisions, and fluency with AI-native practices.