Job Title: Security Architect - AI Products & Multi-Cloud Security
Location: Offshore(Bangalore/Pune/Hyderabad)
Job Summary
We are seeking a skilled Security Architectto ensure the security of our AI-powered products across multi-cloud platforms. This role will focus on implementing end-to-end security practices during the entire software development lifecycle, ensuring data privacy, safeguarding AI models, and promoting Responsible AI practices. You will be instrumental in developing and enforcing security guardrails that protect our AI solutions from potential threats and vulnerabilities.
Key Responsibilities
Application Security: Develop security policies and practices for AI and ML models. Conduct security assessments, code reviews, and threat modeling for AI applications. Implement security measures following OWASP Top 10guidelines to prevent common vulnerabilities. DevSecOps: Integrate security into CI/CD pipelines to enable automated security testing. Use tools like GitHub Actions, Jenkins, and Terraformto automate infrastructure security checks. Promote secure coding standards and practices across development teams. Data Security: Design and implement data protection mechanisms such as encryption (both at rest and in transit) and data anonymization techniques. Ensure compliance with data privacy regulations such as GDPRand CCPA. Utilize tools like Data Loss Prevention (DLP)and data maskingtechnologies for sensitive data protection. Identity & Access Management (IAM): Develop and enforce IAM strategies across multi-cloud platforms (AWS, Azure, GCP). Implement Zero Trust Architectureand role-based access controls (RBAC) to safeguard user access. Utilize multi-factor authentication (MFA)and identity federation protocols. AI Security & AI Guardrails: Define AI guardrails to mitigate risks like model drift, bias, adversarial attacks, and unauthorized model access. Implement AI model monitoring tools like LIME, SHAP, and IBM AIF360for model interpretability and fairness. Promote Responsible AIpractices, ensuring ethical AI deployment and compliance with industry standards. Cloud Security: Architect and implement secure cloud environments using AWS, Azure, and GCPservices. Leverage cloud-native security tools such as AWS Shield, Azure Security Center, and Google Security Command Center. Conduct regular cloud security audits and vulnerability assessments. Compliance & Governance: Ensure alignment with security and compliance frameworks like NIST, ISO 27001, and SOC 2. Lead security audits and penetration testing to identify and mitigate vulnerabilities. Establish security policies and guidelines to ensure organizational compliance. Technical Skills Required
3+ yearsof experience in Data Privacy,cybersecurity, focusing on AI and cloud security. Hands-on experience with one major cloud (AWS, Azure, or GCP) or preferably multi-cloud security(AWS, Azure, GCP)and AI model governance. Strong knowledge of DevSecOpspractices and automated security testing. Proficiency with AI/ML security frameworksand tools for monitoring and securing AI models. Experience with security tools like Burp Suite, OWASP ZAP, and SonarQube. Familiarity with AI ethics, model explainability tools (e.g., LIME, SHAP), and AI risk management. Strong understanding of Privacy by Design Principle, data privacy regulations(GDPR, CCPA) and data security best practices. Knowledge of identity managementsolutions and best practices in IAM. Strong knowledge of Data lifecycle management in AI context. Preferred Qualifications
Certified Information Systems Security Professional (CISSP)Certified Cloud Security Professional (CCSP)AWS Certified Security - SpecialtyAzure Security Engineer AssociateCertified AI Ethics & Governance ProfessionalSoft Skills
Excellent communication skills to collaborate with cross-functional teams, including Data Science, DevOps, and Product Management. Strong analytical and problem-solving abilities. Proven ability to stay updated with the latest security trends, AI regulations, and cloud technologies. Ability to articulate security concepts and practices to both technical and non-technical stakeholders. Nice-to-Have
Experience with Machine Learning Operations (MLOps)security. Hands-on knowledge of Container Security(Docker, Kubernetes). Familiarity with AI ethics frameworksand AI safety research. Exposure to Responsible AItools and methodologies. 
