Work Mode: Remote

Role & responsibilities

AI Architectures

- Train the entire business on safe AI usage that adheres to privacy and cybersecurity goals.
- Coordinate with parent company on AI solutions, vetting strategies, security and privacy governance and guidance.
- Design solutions for LLM usage and AI-generated software that can be easily adopted by the business.
- Analyze and support proposed AI projects companywide, serving as the technical reviewer for the AI Center of Excellence.

AI Security, Privacy & Governance

- Architect security and privacy controls for AI/ML solutions, including data and model pipelines, AI-enabled product features, cloud services, and customer-facing surfaces.
- Define secure-by-design and privacy-by-design patterns; translate regulatory and policy requirements (GDPR, DPDPA 2023, NIST AI RMF, ISO 42001) into actionable engineering guidance.
- Lead hands-on PIA, DPIA, and TPRA assessments for AI/ML systems and data platforms, identifying risks including re-identification, inference attacks, model inversion, and consent gaps.
- Review AI model cards, training data sourcing practices, and automated decision-making workflows to surface privacy, fairness, and transparency risks.
- Serve as a subject-matter expert on privacy-by-design principles as applied to generative AI, LLMs, recommendation systems, and other deployed AI tools.

Risk Management & Assurance

- Define AI risk and control frameworks aligned to the enterprise risk model; drive mitigations, documentation standards, and risk acceptance packages.
- Support the risk register and compliance calendar for AI security and privacy activities, tracking open issues,



remediation actions, and regulatory deadlines.
- Prepare and present governance status reports, risk summaries, and architectural decision records to senior leadership and relevant committees.
- Support vendor and third-party AI tool assessments, including contractual data processing reviews, security questionnaires, and due diligence activities.

Project Management & Cross-Functional Leadership

- Lead end-to-end governance and architecture projects from scoping and stakeholder alignment through implementation and post-deployment review on time and within defined parameters.
- Integrate AI security and privacy requirements into the secure SDLC, architecture review boards, go-to-market readiness gates, and customer/regulatory assurance responses.
- Coordinate with Engineering, Data Science, Legal, Product, and Compliance teams to embed governance checkpoints throughout the AI/ML development lifecycle.
- Manage project plans, milestone tracking, and status reporting using tools such as Jira, Asana, or MS Project; proactively surface blockers and drive resolutions across workstreams.
- Facilitate architecture and risk review sessions, documenting decisions and ensuring follow-through on action items with accountable owners and defined timelines.





KNOWLEDGE & ATTRIBUTES

- Deep analytical and regulatory interpretation skills with the ability to translate complex AI risk requirements into practical, implementable guidance for engineering teams.
- Working knowledge of AI/ML concepts: supervised and unsupervised learning, generative AI, LLMs, automated decision-making, and model governance.
- Structured, methodical approach to project management experienced with agile and waterfall delivery frameworks, comfortable with ambiguity, and adept at prioritizing competing demands across concurrent workstreams.
- Excellent written and verbal communication skills; ability to present complex security and risk topics credibly to both technical and executive audiences.
- Team-oriented, curious, and proactive a self-starter who builds trust with cross-functional peers and drives initiatives through to measurable outcomes.

Preferred candidate profile

- Prior experience of 5+years in privacy, security, and product architecture with accountability for governance and risk decisions.
- Preferred certifications: AIGP or equivalent certification on AI Governance & Privacy.
- Demonstrated hands-on leadership of PIA/DPIA/TPRA and privacy-by-design assessments for AI/ML systems and enterprise data platforms.
- Strong security engineering foundation including secure SDLC, application and product security, and threat and risk assessment methodologies.
- Proven track record leading cross-functional projects with multiple stakeholders; familiarity with project management tools (e.g., Jira, Asana, MS Project) is required.

📌 AI Security & Privacy Architect (Mumbai)
🏢 NTT Global Data Centers
📍 Mumbai