Senior Staff Engineer (AI Developer - AppSec)
ExternalFull-timeOn-site1w ago
AgileAndroidApplication SecurityAWSAzureCI/CD
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Responsibilities
- Design, develop, and maintain AI-powered application security solutions that integrate seamlessly into the software development lifecycle (SDLC).
- Build intelligent SAST automation that contextualizes findings, reduces false positives, identifies root causes, and generates developer-friendly remediation guidance using Large Language Models (LLMs).
- Develop AI-powered secure code review assistants capable of identifying OWASP Top 10 and CWE Top 25 vulnerabilities during pull requests and code reviews.
- Design and implement machine learning models for Software Composition Analysis (SCA), detecting vulnerable dependencies, outdated libraries, malicious packages, and license compliance risks.
- Develop AI-driven DAST orchestration capabilities to automate attack surface discovery, payload generation, vulnerability prioritization, and security testing.
- Build Retrieval-Augmented Generation (RAG) pipelines leveraging internal security knowledge bases, OWASP standards, CVE/NVD repositories, and penetration testing playbooks to provide contextual security guidance.
- Develop agentic AI workflows that automate the complete vulnerability lifecycle, including detection, triage, deduplication, risk scoring, ticket creation, SLA tracking, and remediation validation.
- Design prompt engineering strategies and continuously optimize LLM models for secure code analysis, threat modeling, remediation guidance, vulnerability reasoning, and developer coaching.
- Integrate AI-powered application security capabilities into CI/CD pipelines using platforms such as Jenkins, GitHub Actions, and Azure DevOps to enforce security gates and real-time feedback.
- Develop developer-focused security tooling including IDE extensions, REST APIs, and microservices using FastAPI or Flask to deliver contextual security recommendations.
- Build aggregation platforms that consolidate findings from SAST, DAST, SCA, IAST, and secrets scanning tools into a unified application security risk dashboard.
- Develop intelligent secrets detection capabilities using pattern recognition and AI-based contextual analysis to identify exposed credentials, API keys, and sensitive configuration data.
- Write unit tests, i
Requirements
- Experience : 7.5+years
- Strong experience as an Application Security Engineer, Application Security Developer, or Software Engineer with strong Application Security specialization.
- Strong expertise in Application Security principles, secure SDLC, secure coding practices, vulnerability assessment, and secure code review methodologies.
- Deep knowledge of OWASP Top 10, CWE Top 25, common application vulnerabilities, and secure software development practices.
- Hands-on experience with Application Security toolchains including SAST, DAST, SCA, IAST, and secrets scanning solutions.
- Strong programming skills in Python with experience using AI/ML libraries such as Scikit-learn, PyTorch or TensorFlow, Pandas, and NumPy.
- Experience building AI-powered security automation using Large Language Models (LLMs), Azure OpenAI, OpenAI APIs, prompt engineering, and Retrieval-Augmented Generation (RAG) architectures.
- Experience developing intelligent code analysis, vulnerability detection, remediation recommendation, and AI-assisted security tooling.
- Hands-on experience integrating security tools into CI/CD platforms such as Jenkins, GitHub Actions, and Azure DevOps.
- Experience developing REST APIs and microservices using FastAPI or Flask.
- Good understanding of containerization technologies such as Docker and modern Git-based development workflows.
- Working knowledge of cloud platforms including Microsoft Azure, AWS, or Google Cloud Platform for deploying AI-powered security services.
- Strong understanding of vulnerability management, risk prioritization, remediation workflows, and security automation.
- Familiarity with software composition analysis, dependency management, API security testing, and secrets management.
- Experience with MLOps platforms such as Azure ML, MLflow, or equivalent model deployment and monitoring frameworks.
- Knowledge of LangChain, Semantic Kernel, AutoGen, or similar AI orchestration frameworks is an added advantage.
- Familiarity with OWASP SAMM, BSIMM, software security maturity frameworks, and secure application architecture is preferred.
- Experience with API security testing tools, Postman, REST-assured, or OWASP API Security Top 10 is desirable.
- Exposure to mobile application security testing for Android and iOS platforms is an advantage.
- Strong analytical, troubleshooting, and problem-solving skills with the ability to develop scalable AI-powered security solutions.
- Excellent communication and collaboration skills with experience working in Agile, DevSecOps, and cross-functional engineering teams.
- Bachelor's degree in Computer Science, Information Technology, Engineering, MCA, or a related discipline.
- Professional certifications such as CSSLP, CEH, GWEB, CompTIA Security+, Microsoft Azure AI Engineer Associate, or SC-100 are desirable.
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