AI Security Engineer

We are looking for an experienced AI Security Engineer to join the team. The ideal candidate will specialize in securing AI-powered applications, Large Language Models, AI agents, RAG pipelines, prompt workflows, and enterprise AI platforms. This role focuses on identifying and mitigating AI-specific security risks such as prompt injection, jailbreaks, data leakage, insecure tool usage, unsafe model outputs, and misuse of AI systems. The AI Security Engineer will work closely with AI engineering, platform, cloud, and security teams to design secure AI solutions, define governance controls, and ensure responsible and compliant use of AI-based systems. Experience: 8 - 10 years Responsibilities Design and implement security controls for AI applications, LLM integrations, AI agents, and RAG-based systems Identify and mitigate AI-specific threats such as prompt injection, jailbreaks, data exfiltration, model misuse, hallucination risks, and insecure tool/function calling Secure prompt workflows, system prompts, agent instructions, retrieval pipelines, and AI orchestration layers Assess risks in LLM APIs, vector databases, embeddings, knowledge bases, and external tool integrations Ensure secure handling of sensitive data across AI applications, including user inputs, retrieved context, prompts, and model outputs Conduct threat modeling for AI-based solutions, AI agents, and enterprise AI assistants Define and enforce AI security governance, responsible AI controls, and compliance best practices Perform security assessments, red teaming, and audits of AI systems Monitor emerging AI-related security threats and recommend proactive mitigation strategies Collaborate with engineering teams to secure AI APIs, cloud deployments, access controls, and production AI platforms Requirements Hands-on understanding of AI applications, LLMs, RAG, embeddings, vector search, prompt engineering, and AI agents Experience identifying and mitigating LLM security risks such as prompt injection, jailbreaks, data leakage, and insecure output handling Understanding of secure AI application architecture, including authentication, authorization, logging, monitoring, and data protection Knowledge of cloud platforms such as Azure, AWS, or GCP, especially AI services and API-based deployments Familiarity with security tooling such as SAST, DAST, SIEM, vulnerability scanning, and security monitoring Understanding of data privacy, compliance, and responsible AI practices Experience with AI security frameworks, AI risk management, or responsible AI governance Exposure to LLM red teaming, AI guardrails, content safety, prompt filtering, and output validation Experience securing RAG pipelines, AI agents, tool-calling systems, and enterprise chatbot platforms Knowledge of OWASP Top 10 for LLM Applications or similar AI security guidance Relevant certifications such as CISSP, CEH, Security+, or cloud security certifications Experience in telecom, fintech, enterprise, or regulated environments Strong problem-solving and analytical thinking Ability to work cross-functionally with AI, security, cloud, and product teams Proactive mindset for identifying AI security risks before production impact Effective communication skills to explain risks and mitigation plans to technical and non-technical stakeholders Show more Show less