- Demonstrated ability to lead secure AI engineering at enterprise and multi-client scale
- Strong balance of technical depth, risk judgment, and executive communication
- Proven effectiveness operating in global, regulated, client-delivery environments
- Credibility with both deeply technical teams and non-technical executive stakeholders
TDefine and lead the Secure AI Engineering practice across enterprise and client-delivered AI solutions.
- Establish secure-by-design standards, guardrails, and engineering controls for ML, GenAI, LLM, RAG, and Agentic AI systems
- Translate regulatory and risk requirements into practical engineering standards aligned with business outcomes.
- Oversee security architecture for the end-to-end AI lifecycle—data ingestion, training, fine-tuning, model management, inference, APIs, integrations, and infrastructure.
- Ensure protection against advanced AI threats including data poisoning, model theft, prompt injection, inference attacks, agent misuse, hallucination exploitation, and supply-chain compromise.
- Drive adoption of secure reference architectures, reusable components, and hardened AI pipelines across delivery teams.
- Embed security controls into CI/CD, MLOps, and LLMOps pipelines to enable scale without friction.
- Partner with cyber security and IR teams on AI-related incident preparedness, response, and post-incident improvements
- Client Advisory & External Engagement
- Act as a trusted advisor to business and clients on secure AI architecture, risk posture, and regulatory readiness.
- Lead or support AI security reviews, architecture assessments, and risk discussions for strategic clients.
- Build strong internal capability in secure AI engineering and adversarial ML awareness. solutions etc.
Masters DegBachelor’s or Master’s degree in Computer Science, Cyber Security, AI/ML, Data Science, or related field
10–15+ years of experience in cyber security, secure architecture, or platform engineering, with 3+ years focused on Agentic, AI/ML or GenAI environments
Strong hands-on understanding of cloud-based AI platforms (Azure, AWS, GCP or equivalent)
Experience or strong working knowledge of AI governance, privacy, and MLOps/LLMOps tooling (e.g., Credo.ai, Priva Sapien, model registries and monitoring tools)
Deep knowledge of Secure AI & adversarial ML, Privacy-by-design and data protection, Secure MLOps / LLMOps practices
Familiarity with frameworks and regulations such as NIST AI RMF, NIST CSF, ISO/IEC standards, Emerging global AI regulations (US, EU, sector-specific)
Experience supporting clients in highly regulated industries strongly preferred (preferred) 14 Years