About the Group - Infosys STG

As part of Strategic Technology Group (STG), this role will provide technology leadership for securing next-generation AI, Generative AI and Agentic AI platforms and solutions.

The role will define and drive secure AI architecture, engineering standards, threat modelling, adversarial testing, guardrails and security-by-design approaches across LLMs, RAG platforms, AI agents, AI applications, models, data and MLOps/LLMOps pipelines.

Beyond governance, the role is expected to be deeply technology-led, working with architects, engineering teams, researchers and business stakeholders to identify emerging AI security risks, develop reusable security patterns and accelerators, and translate emerging research into enterprise-grade solutions.

Role Summary :

Leads the enterprise strategy, architecture, governance, and assurance framework for securing AI models, data, pipelines, and agentic systems against evolving threats. The role establishes policy and controls, directs red-team and incident-response programmes, oversees third-party and supply-chain security, and ensures alignment with organizational, regulatory, and ethical expectations.

Key responsibilities :

- Enterprise architecture and policy: Define AI security architecture, standards, policies, and risk controls for models, pipelines, APIs, and agentic systems.

- Threat intelligence: Anticipate prompt injection, model extraction, data poisoning, supply-chain compromise, agent exploitation, and inference-time attacks, and direct mitigation strategies.

- Guardrails and protection: Govern access controls, safety filters, output moderation, LLM firewalls, and behavioral controls.

- Red teaming and assurance: Lead adversarial testing, poisoning and evasion simulations, and robustness assessments.

- Monitoring: Establish telemetry and detection capabilities for anomalous behaviour, misuse, malicious prompts, and AI-specific threat indicators.

- Compliance: Align the security programme with applicable regulation, ISO/IEC 42001, the NIST AI RMF, and internal governance requirements.

- Incident response: Direct triage, forensic analysis, containment, remediation, and communication for AI-related security events.

- Supply-chain security: Establish assurance requirements for third-party models, datasets, APIs, cloud services, and vendors; govern risk acceptance, integration approvals, ongoing monitoring, and escalation of material findings.

- Secure development: Embed secure-by-design practices across training, fine-tuning, deployment, access governance, and MLOps/LLMOps.

- Capability building: Develop playbooks, training, and best-practice guidance for engineering and business teams.

Technical Skills :

- Deep expertise in adversarial ML, LLM and agent security, prompt-injection defence, poisoning, evasion, extraction, and inversion risks.

- Deep expertise on AI model development and its applications.

- Deep knowledge on Threat modeling and industry frameworks such as MITRE ATLAS, OWASP guidance, STRIDE, and the NIST AI RMF.

- Secure AI architecture, including protected inference, guarded prompt interfaces, zero-trust controls, secure APIs, and LLM firewalls.

- Expert AI Security Attack Analysis and Remediation.

- PyTorch, TensorFlow, LLMs, RAG, agentic workflows, and secure MLOps/LLMOps.

- Cryptography, secure coding, identity and access management, audit logging, and cloud security across Azure, AWS, and GCP.

Professional Skills :

- Executive communication and the ability to translate technical risk into clear decisions and actions.

- Strategic, risk-informed judgement under uncertainty.

- Cross-functional leadership across cybersecurity, engineering, legal, product, privacy, and governance.

- Analytical rigor, ethical reasoning, and proactive threat anticipation.

- Academic background in cybersecurity, computer science, engineering, or a quantitative discipline with AI/ML security specialization.