Principal AI Security Architect

Official role: Principal AI Security Architect Trianz requisition: TR25061 Location: Bangalore, India Experience: 14-18 years Posted: 07-Jul-2026

Company Overview

Trianz is an applied AI solutions company that accelerates customer business transformation through AI powered "Transformation Services as a Software Model" . With 25+ years of transforming enterprises, we've evolved to a product-led, platform-driven organization serving global enterprises across Financial Services, Insurance, Healthcare, Hi-Tech, Manufacturing, and other industries.

With global presence across 4 continents, our platform portfolio under the unified Concierto brand delivers end-to-end transformations including solutions for Migrate, Manage, Maximize, Modernize, Insights & Agentic AI, and SecOps - delivered through strategic partnerships with leading hyperscalers.

We're building the premier innovation-led organization in the digital transformation space through AI-first methodologies and data-driven excellence - RevolutionAIzing Transformations.

Role Overview

Most security architects can secure a cloud environment. Most AI engineers understand LLM attack surfaces. Very few can do both ? at enterprise depth, in production, for the most demanding regulated clients in the world. That is exactly what this role requires.

As Principal AI Security Architect in the CTO Office, you sit at the hardest intersection in enterprise AI: production cloud security architecture meets AI-native threat modelling. You are not a consultant who reviews and recommends. You are not a GRC professional who manages frameworks. You are not an auditor who identifies gaps. You are the architect who designs the system that closes them.

Trianz deploys AI inside financial institutions, government agencies, and global enterprises ? environments where a single misconfigured trust boundary or an unguarded prompt injection surface is a commercial and reputational catastrophe. Every AI deployment, every private LLM serving stack, every sovereign customer environment runs against the security architecture you produce.

You will own two domains simultaneously ? and the gap between them is exactly where this role lives. First: the AI-specific attack surface ? prompt injection, model exfiltration, indirect prompt injection, data leakage via LLM outputs, guardrail bypass. Second: the infrastructure security gap that opens after cloud migration ? the blind spot that application security tools don't see and that most post-migration teams leave unaddressed.

You produce the specifications. The AI development team and the product engineering team implement against them. If your instinct is to audit, flag, and hand off ? this is not your role. If your instinct is to threat-model, design the control, write the spec, and verify it closes the gap ? you are exactly who we are looking for

Key Responsibilities

AI Security Architecture

- Design AI-specific threat models: prompt injection, model exfiltration, indirect prompt injection, and data leakage via LLM outputs

- Design LLM guardrail architecture: input validation, output filtering, rate limiting, and abuse detection

- Produce the security architecture specification for the AI platform that the development team implements

- Design the security testing framework for AI systems: red-teaming, adversarial testing, and model robustness evaluation

- Evaluate open-source and commercial AI systems against enterprise security requirements

Zero Trust & Infrastructure Security

- Design Zero Trust architecture for private AI deployments: network segmentation, identity-based access, and least-privilege for model serving

- Own post-migration infrastructure security architecture ? the gap between application security and infrastructure access controls

- Design network security topology for multi-cloud and on-premises AI deployments

- Architect secrets management, IAM, and CSPM frameworks for AI workloads

- Define security standards for Kubernetes-based AI serving infrastructure

Sovereign AI & Compliance Architecture

- Design sovereign AI compliance architecture: DPDP Act (India), GDPR (Europe), SOC2, and government air-gap requirements

- Produce the security compliance documentation pack for enterprise sales engagements

- Conduct security architecture reviews of product modules and identify gaps against enterprise customer standards

- Define data residency and privacy architecture for regulated enterprise verticals

- Architect per-tenant security isolation for multi-tenant AI deployments

Security Governance

- Produce security architecture specifications that engineering teams implement ? not just audit reports

- Conduct design reviews across AI and product modules before implementation

- Maintain the security architecture decision record and standards library

- Define security testing standards: penetration testing, red-teaming, threat modelling cadence

- Enable enterprise sales with compliance documentation and security architecture artifacts

Ideal Candidate Profile

Experience: 15+ years

Cloud Security Architecture:

- Designed enterprise cloud security architecture in production: IAM, network segmentation, secrets management, CSPM

- Hands-on AI or LLM security threat modelling ? prompt injection, model exfiltration, or data privacy in AI contexts

- Produced security architecture specifications that engineering teams implemented ? not just audits

- Zero Trust implementation experience: BeyondCorp, SPIFFE/SPIRE, service mesh security, or equivalent

- Compliance framework depth in a hands-on architecture capacity: GDPR, SOC2, or equivalent

- 10+ years in security architecture with at least 2 years in AI/ML security contexts

Technical Depth

- Deep expertise in cloud security across AWS, Azure, and GCP: IAM, VPC design, security groups, CSPM tooling

- Strong command of AI/LLM-specific attack surfaces: OWASP LLM Top 10, prompt injection variants, training data poisoning

- Hands-on experience with Zero Trust frameworks: identity-based access, microsegmentation, mutual TLS

- Understanding of model serving security: inference endpoint protection, model weight security, API abuse detection

- Knowledge of cryptographic controls: encryption at rest and in transit, key management, HSM integration

- Proficiency in security architecture documentation: threat models, data flow diagrams, trust boundary maps

Specification Quality

- Produces implementable security architecture ? not slide decks or audit checklists

- Writes security specifications that engineering teams act on independently

- Builds compliance documentation that satisfies enterprise security questionnaires and sales cycles

- Designs security controls that are specific, testable, and verifiable

Mindset & Fit

- Architect-first mindset: identifies the gap and designs the solution in the same motion

- Comfortable owning both the AI security and infrastructure security domains simultaneously

- High written communication standard ? your specs are reviewed by enterprise CISOs

- Motivated by closing real security gaps in production AI systems, not theoretical risk scoring

- Driven by the intersection of emerging AI threats and enterprise security requirements

Good to Have

- DPDP Act (India) compliance architecture experience

- OWASP LLM Top 10 and AI security research frameworks (MITRE ATLAS, NIST AI RMF)

- Confidential computing: Intel TDX, AMD SEV, or AWS Nitro Enclaves

- AI system penetration testing or red-teaming ? hands-on adversarial evaluation

- Financial services or government security compliance ? FedRAMP, PCI-DSS, RBI guidelines

- eBPF-based security tooling ? runtime threat detection for containerized AI workloads

- Supply chain security ? model provenance, SBOM for AI systems, dependency integrity

- Formal threat modelling frameworks ? STRIDE, PASTA, LINDDUN for privacy threat modelling

Why Join Trianz?

Architectural Impact: Own the complete private AI infrastructure for Fortune 500 enterprises. Your decisions influence how LLMs run in the most security-conscious, regulated environments globally.

Technical Excellence: Work with cutting-edge inference frameworks, open-source models, and heterogeneous hardware. Design systems that work across AWS, Azure, GCP, on-prem, and air-gapped environments ? not just API consumption.

Sovereign AI Leadership: Lead the charge in sovereign AI deployment. Help enterprises reclaim control of their AI infrastructure while maintaining the flexibility to scale globally.

Zero Bureaucracy: Pure IC role with architectural authority. No slow approval cycles ? your design decisions move fast into production.

Enterprise Scale: Work on transformations across Fortune 500 organizations. See your architecture deployed across continents, industries, and mission-critical use cases.

Growth Through Ambiguity: Thrive in the emerging sovereign AI space where problems are complex, standards are evolving, and your expertise will define the industry.