Engineering Division - Tech Risk Advisory -Vice President- Hyderabad/Bangalore
Goldman Sachs · Bangalore, India · 1mo ago
SeniorOn-siteSoftware Engineering
Responsibilities
- Conduct comprehensive cloud security assessments, evaluating designs, configurations, and implementations across major cloud service providers (CSPs) including AWS, Azure, and GCP.
- Architect and drive enterprise-wide cloud security strategies, including baselines, guardrails, and secure design patterns for cloud-native and hybrid environments.
- Identify and analyze potential security risks, vulnerabilities, and misconfigurations within cloud environments, AI/ML platforms, and applications.
- Perform software architecture design reviews for cloud deployments, including AI/ML pipelines, LLM integrations, agentic frameworks, and data platforms.
- Develop and enforce security controls for AI/ML systems, covering model security, data governance, prompt injection defenses, supply chain integrity, and inference infrastructure hardening.
- Collaborate with AI engineering, platform, and development teams to embed security throughout the SDLC and CI/CD pipelines, including AI-specific development workflows.
- Develop, evaluate, and document security measures, controls, and guardrails to protect data, applications, APIs, and infrastructure across cloud and AI environments.
- Provide senior technical advisory services on cloud security, AI security, and security engineering to internal stakeholders, ensuring alignment with firm-wide security policies and industry best practices.
- Develop and maintain scripts, automated solutions, and security tooling to streamline security processes, vulnerability identification, and compliance checks across cloud and AI environments.
- Stay current on emerging threats across cloud, AI/ML, and security engineering domains, including adversarial ML techniques, cloud-native attack vectors, and evolving regulatory requirements.
- Lead and mentor technical security teams; influence senior stakeholders and align security posture with business objectives.
- Contribute to incident response and remediation efforts related to cloud security and AI security events as required.
- 12+ years of hands-on experience in cybersecurity, with depth spanning in the following domains:
- Cloud Security: Architecting and assessing security for cloud-native and hybrid environments across major CSPs (AWS, Azure, GCP, OCI).
- Security Engineering: Building security tooling, automation, detection capabilities, or secure-by-design systems at scale.
- AI Engineering Security: Securing AI/ML systems, LLM-based applications, agentic pipelines, or ML infrastructure.
- Cybersecurity Generalist: Broad cross-domain expertise including network security, identity and access management, threat modeling, vulnerability management, incident response, and compliance.
- Strong development and scripting proficiency (Python, PowerShell, Bash, or similar) for automation, security tooling, and data analysis.
- Deep, demonstrated knowledge of cloud security architecture across at least one major CSP (AWS strongly preferred), including IAM, network security, encryption/key management, workload/container security, and monitoring/logging.
- Technical expertise in application security architecture, including secure-by-design patterns, threat modeling, and enterprise AppSec standards for web, API, and microservices environments.
- Proven experience securing AI/ML systems or platforms, including familiarity with threats specific to LLMs, model pipelines, and AI supply chains.
- Proven track record driving security initiatives across large, complex enterprise environments with cross-functional impact.
- Experience in financial services or other highly regulated industries, with familiarity with relevant compliance frameworks.
- Advanced knowledge of industry security frameworks and standards (e.g., NIST AI RMF, NIST CSF, ISO 27001, CIS Benchmarks, MITRE ATLAS, OWASP LLM Top 10).
- Familiarity with AI/ML security frameworks including OWASP LLM Top 10, MITRE ATLAS, and NIST AI Risk Management Framework.
- Strong leadership and communication skills to influence at the executive level, mentor technical teams, and represent security in cross-functional forums.
- Relevant certifications across security and cloud domains (e.g., CISSP, CCSP, AWS Certified Security – Specialty, Azure Security Engineer Associate, Google Cloud Professional Cloud Security Engineer, GIAC certifications).
- Experience with MLOps, model deployment pipelines, and AI platform security (e.g., SageMaker, Vertex AI, Azure ML, Databricks).
- Hands-on experience with red teaming, or adversarial testing of AI/ML systems.