AI Security Engineer

Job Description

Key Responsibilities : AI-Powered Developer Workflow

• Analyze and map end-to-end developer workflows across planning, coding, testing, CI/CD, deployment, and operations.
• Design, build and assess AI-powered solutions that improve developer productivity and engineering efficiency.
• Develop AI agents and copilots for product security team to automate repetitive engineering and security tasks.
• Implement Retrieval-Augmented Generation (RAG) and knowledge systems that leverage internal documentation, security standards, and best practices.
• Build integrations across developer tools including GitHub, GitLab, Jira, Azure DevOps, Jenkins, and IDE platforms.
• Identify bottlenecks in development workflows and recommend AI-based process improvements complying with security standards.

Application Security

• Serve as the Application Security Subject Matter Expert (SME) for AI-enabled development initiatives.
• Embed security controls and guidance directly into developer workflows.
• Define approaches for secure code generation and AI-assisted software development.
• Partner within Product Security teams to integrate SAST, DAST, SCA, secrets detection, IaC scanning, and container security into AI solutions.
• Develop security guardrails for AI-generated code and AI agents.
• Evaluate and mitigate risks associated with LLMs, AI agents, and autonomous software development.
• Drive Secure SDLC and DevSecOps best practices across engineering organizations.

AI Solution Development

• Design, develop, and deploy production-grade application using C#, Asp.net, SQL server, AI applications using modern AI frameworks.
• Partner with business units to build and assess agentic workflows leveraging LLMs and orchestration frameworks.
• Create evaluation frameworks to measure AI model effectiveness, security, and developer adoption.
• Work with large-scale datasets and telemetry to generate actionable engineering insights.
• Develop APIs, services, and integrations supporting enterprise AI Collaboration :
• Collaborate with Engineering, Product Security, Platform Engineering, DevOps, and Developer Experience teams.
• Present findings and recommendations to technical leaders and executives.
• Influence enterprise AI strategy for secure software development.

Required Qualifications Experience :

• 7+ years of software engineering, security engineering, or related experience.
• 3+ years developing AI, ML, or GenAI solutions in enterprise environments.
• Hands-on experience with developer platforms and modern software delivery practices.
• Prior experience in Application Security, Product Security, DevSecOps, or Secure SDLC programs.

Technical Skills

• Strong programming skills in Python, Java, JavaScript/TypeScript, or Go.
• Experience with LLM platforms such as Azure OpenAI, OpenAI, Anthropic, or similar.
• Expertise in AI agent frameworks and orchestration platforms.
• Experience with vector databases, embeddings, RAG architectures, and knowledge retrieval systems.
• Strong understanding of APIs, microservices, and cloud-native architectures.
• Experience with Azure, AWS, or Google Cloud.

Application Security Expertise

• Deep understanding of Secure SDLC, Threat Modeling, OWASP Top 10, SAST, DAST, IAST, Software Composition Analysis (SCA), Secrets Management, Secure Coding Practices.
• Ability to review code and identify security vulnerabilities.
• Familiarity with security tools such as Checkmarx, Veracode, Fortify, Snyk, GitHub Advanced Security, Semgrep, SonarQube, or similar. (ref:hirist.tech)