AI Security Engineer

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 capabilities.

Cross-Functional 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.