This role pays more than
94%
of similar roles. Most pay
$151,475–$214,000
— the shaded band above.
At the midpoint, this role pays about
$244k
versus about
$183k
for comparable roles.
Based on 240 similar postings.
Employer
About Dell Technologies
Dell Technologies is a US-based technology company that designs and sells PCs, servers, storage and networking hardware, along with related software and IT services. Its product lines include the XPS, Latitude and Alienware PCs and the PowerEdge server and PowerStore storage families.
Dell Technologies currently has
76 open roles
on FindRole.
Listed pay typically runs
$174,000–$235,000
across 73 roles with salary data.
AI Solution Engineer, Security serves as the design authority for the Dell Security & Resiliency team to reconstitute security work as AI-native capabilities. This role focuses on consolidating fragmented security processes into unified workflows that integrate directly into business and engineering pipelines rather than acting as manual checkpoints. The engineer will design agentic system architectures, including agent topology, determination of logic boundaries, and defense against prompt injection or tool abuse. Key responsibilities include establishing reference patterns for orchestration, evaluation harnesses, and integration via APIs and Model Context Protocol. Candidates must possess deep expertise in agent loop design, context engineering, and production economics like token budgeting. Required skills include proficiency in Python, TypeScript, or Go, along with experience in distributed systems, data modeling, and security domains such as vulnerability management or threat modeling to solve complex infrastructure challenges.
Design AI-native security workflows from problem statements to buildable specifications including agent topology and logic boundaries.
Determine which tasks should be handled by agents, deterministic automation, or left as manual processes.
Consolidate fragmented security functions into unified, automated processes that eliminate redundant work across teams.
Define strict authority boundaries and technical controls for AI agents to ensure secure execution and limited blast radii.
Threat model agentic systems against risks like prompt injection, tool abuse, and data exfiltration.
Establish a reusable foundation of reference architectures, agent primitives, and evaluation harnesses for production use.
Integrate agentic workflows with existing security tools, identity systems, and data platforms using APIs and event streams.
Baseline current manual cycle times to measure the measurable impact of new automated workflows.
What we're looking for
12+ years of experience in software engineering, security engineering, or platform architecture with a record of production delivery.
Experience delivering AI and agentic systems into production with measurable operational or business impact.
Deep hands-on capability in agentic system engineering including loop design, context engineering, determinism boundary design, evaluation engineering, and production economics.
Ability to threat model agentic systems against prompt injection, tool abuse, untrusted content, and data exfiltration.
Strong programming proficiency in Python, TypeScript/JavaScript, Go, or similar languages for designing, prototyping, and reviewing code.
Experience in distributed systems, integration engineering, and data modeling across systems of record.
Demonstrated experience leading cross-functional process consolidation and influencing stakeholders at both engineer and executive levels.
Working knowledge of security engineering domains such as application security, vulnerability management, or threat modeling.
Bachelor’s or master’s degree in computer science or a related field, or equivalent practical experience.
Experience applying agentic AI within a security domain (preferred).
Experience building MCP servers, agent skills/tools, or extending AI coding assistants (preferred).
Familiarity with secure-by-design principles and frameworks like OWASP Top 10 for LLM Applications or NIST standards (preferred).
Experience with production LLMOps including versioning, regression detection, and behavioral drift monitoring (preferred).
Prior senior individual contributor role as a Principal or above with organization-wide design authority (preferred).