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AI Security Engineer
Job in
Round Rock, Williamson County, Texas, 78682, USA
Listed on 2026-10-13
Listing for:
Perfict
Full Time
position Listed on 2026-10-13
Job specializations:
-
Software Development
AI Engineer (Applied/Software), Backend Developer
Job Description & How to Apply Below
- Design, develop, test, and deploy AI-powered and autonomous agent solutions for enterprise business and security workflows.
- Build agentic workflows, agent loops, multi-agent systems, sub-agents, and tool/function-calling capabilities.
- Translate business and stakeholder requirements into secure, scalable, and production-ready AI solutions.
- Take ownership of the complete solution lifecycle, including requirements gathering, architecture, development, testing, deployment, monitoring, and adoption.
- Develop AI capabilities that support the SDLC, including:
- AI-generated code assurance
- Security guardrails and policy enforcement
- Automated design and security reviews
- Dependency and software supply-chain analysis
- Static and dynamic analysis orchestration
- Release and compliance audit support
- Integrate AI solutions with enterprise platforms such as source control, CI/CD pipelines, ticketing systems, security scanning tools, identity platforms, cloud services, and internal applications.
- Develop integrations using REST/GraphQL APIs, webhooks, MCP (Model Context Protocol), and other enterprise protocols.
- Apply strong software architecture and systems-design principles, including service boundaries, data modeling, observability, scalability, extensibility, and secure defaults.
- Engineer effective context management, including retrieval, search, memory architectures, enterprise data grounding, and structured outputs.
- Optimize LLM token consumption, context windows, cost, latency, and overall production performance.
- Develop and implement AI evaluation practices to measure accuracy, reliability, safety, quality, and business impact.
- Evaluate emerging AI technologies, agentic frameworks, retrieval and memory systems, evaluation frameworks, guardrails, and LLMOps capabilities.
- Partner with security, engineering, product, architecture, and leadership teams to evaluate technical trade-offs and drive adoption.
- Establish reusable AI engineering patterns, standards, documentation, and best practices.
- Promote secure-by-design and secure-by-default principles throughout AI solution development.
- Mentor team members and contribute to technical enablement through documentation, demonstrations, and knowledge sharing.
- Proven experience developing and deploying AI/LLM solutions into production environments.
- Hands-on experience building autonomous AI agents or agentic workflows for enterprise business p
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