Generative AI Security Engineer – Agentic AI & Secure SDLC Automation

Iconma Portal•Phoenix, AZ

2d•Hybrid

About The Position

Our client, an IT Services and Consulting company, is seeking a Senior Engineer-Generative AI Automation for a Phoenix, AZ/hybrid location. The role serves as a subject matter expert in Generative AI and agentic engineering, contributing technical expertise and leadership to enterprise security initiatives. The engineer will build, integrate, manage, monitor, and report on production-grade AI agents and security automation, helping reduce Generative AI and Agentic AI security risks and supporting the security champion practice by promoting security principles and controls. The position designs, builds, and operationalizes autonomous AI agents for secure, scalable enterprise automation. It embeds security across the software development lifecycle through secure design reviews, threat modeling, code review, dependency and vulnerability analysis, remediation guidance, pull-request checks, and release/security gates. Agents must interact securely with source control, CI/CD, issue tracking, security scanners, cloud services, internal APIs, and developer tooling while enforcing least privilege, secrets protection, authorization boundaries, auditability, and approval workflows. The engineer establishes agent quality and security evaluation practices, including task success, groundedness, code quality, secure coding, prompt-injection resistance, data leakage, unsafe tool use, permissions, reliability, latency, and cost. The role applies secure-by-design practices to AI-assisted development through sandboxing, policy enforcement, telemetry and observability, traceability, red-team testing, and controls for agent-generated code before merge or deployment. It also provides technical leadership and mentorship in agent architecture, AI-assisted engineering, secure coding, and production readiness, and conducts research on emerging threats, vulnerabilities, and technologies.

Requirements

Experience building automation tooling for security or developer efficiency.
Proficiency in at least one scripting language: Python, JavaScript, or Bash.
Familiarity with at least one compiled language: Go, Rust, or Java.
Experience with GitHub automation and API integrations.
Strong understanding of OWASP Top 10 and industry best practices in Generative AI security.
Knowledge of agent frameworks such as LangGraph, AutoGen, CrewAI, or custom orchestration.
Understanding of AI lifecycle management, including model integration, evaluation, and observability.
Senior-level experience designing, building, and productionizing autonomous and semi-autonomous AI agents, including planning, tool use, memory/context management, multi-agent orchestration, human-in-the-loop controls, and failure recovery.
Hands-on experience with AI coding and agent platforms such as Anthropic Claude/Claude Code and OpenAI Codex, including prompt/instruction design, tool integration, API/SDK usage, evaluation, guardrails, and enterprise adoption patterns.
Experience creating reusable agent tools, skills, workflows, and orchestration patterns using frameworks such as LangGraph, AutoGen, CrewAI, custom orchestration, and tool-protocol patterns such as Model Context Protocol (MCP).
Experience performing proofs of concept related to security tooling, zero-day vulnerabilities, and emerging risks.
Familiarity with Oracle, SQL, and NoSQL database architectures.
At least 10 years of automation and development experience in CI/CD.
At least 10 years of software engineering experience.
At least 5 years of cybersecurity experience.
At least 2 years of Generative AI experience.
Hands-on experience delivering agentic AI or AI coding-agent solutions into production, preferably in security, developer productivity, or Secure SDLC workflows.
Deep expertise in security automation tooling, CI/CD and pull-request security integrations, secure-by-design AI architectures, and embedding security throughout the software lifecycle.
Bachelor’s degree in Cybersecurity, Data Science, Computer Science, or Software Engineering.
Strong knowledge of adversarial robustness techniques and tools for machine learning.
Strong knowledge of AI risk management frameworks and Trustworthy AI practices.

Nice To Haves

Master’s degree in Cyber Security, Data Science, Computer Science, or Software Engineering.
Information security certifications are encouraged, such as SANS GIAC, CISSP, or Generative AI certifications.
Machine Learning Operation professional certifications.

Responsibilities

Design, build, and operationalize autonomous AI agents that enable secure, scalable, and intelligent enterprise automation.
Integrate agents with enterprise systems, APIs, data sources, and security platforms.
Build Secure SDLC agents that support requirements and secure design reviews, threat modeling, code review, dependency and vulnerability analysis, remediation guidance, pull-request checks, and release/security gates.
Design agents to interact securely with source control, CI/CD, issue tracking, security scanners, cloud services, internal APIs, and developer tooling.
Enforce least privilege, secrets protection, authorization boundaries, auditability, and approval workflows in agent integrations.
Establish agent quality and security evaluation practices covering task success, groundedness, code quality, secure coding, prompt-injection resistance, data leakage, unsafe tool use, permissions, reliability, latency, and cost.
Apply secure-by-design engineering to AI-assisted development, including sandboxing, policy enforcement, telemetry and observability, traceability, red-team testing, and controls for agent-generated code before merge or deployment.
Develop and maintain security automation tooling to improve visibility, enforce security best practices, and streamline processes.
Automate secure design reviews and integrate security checks into pull-request workflows and CI/CD pipelines.
Automate integrations and contribute to managing, monitoring, and reporting on Generative AI and Agentic AI security risk reduction.
Support the security champion practice by promoting security principles and controls.
Conduct research on emerging threats, vulnerabilities, and technologies to stay current with the security landscape.
Provide technical leadership and mentorship in agent architecture, AI-assisted engineering, secure coding, and production readiness.
Translate emerging agent capabilities into scalable enterprise engineering patterns.
Perform proofs of concept related to security tooling, zero-day vulnerabilities, and emerging risks.
Mentor team members in application security and secure development practices.