Security Architect

Job Description &

Summary:

IFS II AI Application Security Architect II Manager II PAN India

The AI Application Security Architect is responsible for ensuring that AI platforms, applications, and AI-enabled products are designed, built, and operated securely. This role partners closely with AI platform engineering teams, product teams, data science, Dev Sec Ops , cloud security, and compliance stakeholders to embed security into every stage of the AI development lifecycle.
The architect will gain a deep technical understanding of each AI system—its architecture, data flows, model behavior, and integration points—and will use this context to conduct design reviews, threat modeling, risk assessments, and guide secure development practices. They will help teams identify when design or implementation changes require deeper security review, validation, or security-enhancing controls.

Responsibilities:

1. Security Architecture & Risk Assessment

- Develop and maintain security architecture standards and patterns for AI and ML systems.
- Perform detailed architecture analysis and threat modeling for AI systems.
- Provide contextualized risk assessments based on system design, data types, deployment model, and integration patterns.
- Support teams in making secure design decisions and selecting appropriate guardrails and mitigations.
2. Engagement With AI Platform, Application, and Product Teams

- Partner with engineering, product managers, and data scientists to deeply understand how AI capabilities are built and consumed.
- Develop repeatable engagement models for reviewing new AI features, architectural changes, and emerging risks.
- Serve as a trusted advisor to teams, offering actionable guidance that balances innovation, speed, and security.

3. Dev Sec Ops  Integration
- Embed secure coding and model development practices into CI/CD pipelines for AI workloads.
- Define and enforce automated security checks for data ingestion, model training, model deployment, inference APIs, and RAG pipelines.
- Work with Dev Ops and platform engineers to integrate security tooling.
- Continuously improve security testing for AI systems through red-teaming and prompt-based evaluation.

4. Governance, Compliance, and Policy Alignment
- Contribute to governance for responsible AI and AI security frameworks.
- Ensure AI implementations meet internal policies and regulatory requirements.
- Develop documentation for secure design principles, reference architectures, and risk remediation plans.

5. Monitoring, Incident Preparedness, and Continuous Improvement
- Guide teams on logging, monitoring, and anomaly detection for AI behaviors.
- Support incident response for AI-related security events.
- Identify emerging threats and update architecture patterns accordingly.
- Promote internal knowledge sharing and capability building.

Mandatory skill sets:

Technical Expertise
- Strong background in application security, cloud security, or security architecture.
- Hands-on knowledge of AI/ML systems and lifecycle.
- Experience securing LLM-based systems, APIs, RAG pipelines, and vector stores.
- Deep familiarity with AI security risks.
- Skilled in threat modeling methodologies.
- Knowledge of modern Dev Sec Ops  tools.
- Familiarity with major cloud platforms.

Preferred skill sets:

Experience designing or securing generative AI applications.
- Familiarity with NIST AI RMF, OWASP LLM Top 10, ISO/IEC 42001, MITRE ATLAS.
- Security or cloud certifications.
- ML engineering or MLOps experience.

Soft Skills
- Excellent communication skills.
- Strong stakeholder management.
- Curiosity and ability to dive deep into AI technologies.
- Comfortable working in fast-paced environments.

Years of experience required:

7+ yrs
Education qualification:

Any Ug/Pg