1 day ago
Noida, IndiaStaff+
Responsibilities
- Define 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 across the AI lifecycle, including data ingestion, training, fine-tuning, model management, inference, APIs, integrations, and infrastructure.
- Protect AI systems against 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.
- Partner with cybersecurity and incident response teams on AI incident preparedness, response, and post-incident improvements.
- Advise business leaders 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 internal capability in secure AI engineering and adversarial ML awareness.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Cyber Security, AI/ML, Data Science, or a related field.
- 10–15+ years of experience in cybersecurity, secure architecture, or platform engineering.
- At least 3 years of experience focused on agentic, AI/ML, or GenAI environments.
- Strong hands-on understanding of cloud-based AI platforms such as Azure, AWS, and GCP.
- Experience or strong working knowledge of AI governance, privacy, and MLOps/LLMOps tooling, including Credo.ai, Priva Sapien, model registries, and monitoring tools.
- Deep knowledge of secure AI, adversarial ML, privacy-by-design, data protection, and secure MLOps/LLMOps practices.
- Familiarity with NIST AI RMF, NIST CSF, ISO/IEC standards, and emerging US, EU, and sector-specific AI regulations.
- Strong technical depth, risk judgment, executive communication, and credibility with technical and non-technical stakeholders.
- Experience supporting clients in highly regulated industries is strongly preferred.
Tech Stack
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About EXL
EXL provides data analytics, AI, and digital operations services for enterprises in insurance, healthcare, banking, and other regulated industries. It delivers consulting, managed services, and technology platforms to improve decision-making, risk management, and operational efficiency. Founded in 1999 and headquartered in New York, EXL is a public company listed on the NASDAQ (ticker: EXLS).