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.
- Robust 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)

📌 AI Security Engineer (Hyderabad)
🏢 India Fan
📍 Hyderabad