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Manager | Hybrid cloud | Bengaluru | Engineering | Hybrid Cloud Engineering at Deloitte South Asia · Hyriko
via Career pages · 3w ago
Manager | Hybrid cloud | Bengaluru | Engineering | Hybrid Cloud Engineering Deloitte South Asia
Full-time Hybrid
Location: Bengaluru, India Type: Full-time Posted: 3w ago
Manager | Hybrid cloud | Bengaluru | Engineering | Hybrid Cloud Engineering
Job requisition ID : 107229 Location: Bengaluru Entity: Deloitte Touche Tohmatsu India LLP Job Title: Manager – Security & Compliance Architect (AI Infrastructure)
Role Overview We are seeking a Manager-level Security & Compliance Architect to design and implement secure, compliant, and resilient AI infrastructure platforms, including GenAI, ML pipelines, and data ecosystems.
This role will focus on embedding security-by-design and compliance-by-default principles across AI systems, ensuring protection of data, models, and infrastructure while aligning with regulatory and industry standards.
Key Responsibilities AI Security Architecture Design and implement end-to-end security architecture for AI/ML and GenAI platforms: Model training and inference environments Data pipelines, vector databases, and orchestration frameworks - Define secure reference architectures for: Cloud-native AI platforms (Azure, AWS, GCP) Hybrid and multi-cloud deployments Implement defense-in-depth strategies across AI systems AI-Specific Threat Modeling & Risk Management
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Get email alerts Conduct threat modeling for AI systems covering: Prompt injection and jailbreaking Data leakage and inference attacks Identify and mitigate AI-specific vulnerabilities across: Model artifacts and endpoints Perform risk assessments and define mitigation strategies aligned to enterprise risk appetite Ensure AI platforms adhere to global and regional standards such as: ISO 27001, SOC 2, NIST, CIS benchmarks GDPR, HIPAA (as applicable) Emerging AI regulations (e.g., EU AI Act, responsible AI guidelines)
- Define and implement: Data governance and privacy frameworks Model governance and lifecycle controls Support audit readiness, compliance reporting, and certifications Identity, Access & Data Security
- Define and implement: Zero Trust architecture for AI platforms Fine-grained access controls (RBAC/ABAC)
- Secure: Training and inference data Secrets, tokens, and embeddings
- Implement encryption strategies: Data at rest and in transit Secure key management (HSM, KMS) Secure AI Development & MLOps
- Embed security into: CI/CD and MLOps pipelines Model development and deployment lifecycle
- Implement: Secure coding and model development best practices Dependency and artifact security (SBOMs, vulnerability scanning)
- Establish controls for: Model versioning and integrity Monitoring, Detection & Incident Response Design security monitoring for AI platforms: Anomalies in model outputs Data exfiltration attempts Unauthorized access patterns
- Integrate with enterprise: Threat intelligence systems Define incident response plans for AI-specific risks Conduct security drills and simulations Tooling & Platform Enablement Implement and manage security tools such as: Cloud-native security (Defender, GuardDuty, Security Command Center) Container security (Aqua, Prisma, etc.) Evaluate and integrate AI security tools (prompt filtering, model monitoring, adversarial testing) Build automated guardrails using policy-as-code
- Work with: Data science and platform teams Enterprise security and compliance groups Translate technical risks into business impact and compliance needs
- Support leadership with: Security posture reporting Risk dashboards and remediation plans
- 8–12 years of experience in: Cybersecurity architecture / cloud security Compliance and risk management 3–5+ years in cloud-native or AI/ML environments Hands-on experience in designing secure distributed systems
- Deep understanding of: Security architecture principles (Zero Trust, defense-in-depth) Cloud security frameworks and controls Compliance standards and regulatory frameworks
- Strong knowledge of: AI/ML lifecycle and associated risks Data security and privacy engineering Cloud Platforms: Azure, AWS, GCP
- Security: IAM, encryption, network security, secrets management
- AI/ML: LLM APIs, model pipelines, data pipelines
- DevSecOps: CI/CD security, SAST/DAST, container security
- Tools: SIEM (Splunk, Sentinel), vulnerability management, API security Leadership & Consulting Skills
Strong stakeholder management and communication skills Ability to translate security into business and compliance outcomes Experience working in cross-functional teams and transformation programs
- CISSP, CISM, CCSP Azure Security Engineer / AWS Security Specialty
- Exposure to: Responsible AI frameworks Privacy-enhancing technologies (PETs)
- Experience in: Multi-cloud and regulated environments (BFSI, healthcare, etc.)
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