Cyber Security AI Architect

Cyber Security AI Architect Role: Cyber Security AI Architect Experience: 713 Years Location: Pune Employment Type: Full-Time Work MOde: 5 days onsite, shift timing US shift 6:30PM to 2"30PM Primary Purpose The Cyber Security AI Architect is responsible for reviewng and designing secure architectures for enterprise AI systems and translating those designs into practical controls, patterns, and implementation guidance. The role leads security thinking across the AI lifecycle, including model selection, retrieval and orchestration layers, agentic workflows, data protection, identity and access management, monitoring, red teaming, and incident response. This person works closely with platform, software engineering, product, engineering, security operations, data, legal, and governance stakeholders to reduce AI risk without slowing delivery. Major Responsibilities Define secure reference architectures for LLM, RAG, agentic, and other AI enabled systems across cloud and enterprise environments. • Lead AI threat modeling and identify design controls for prompt injection, insecure output handling, excessive agent permissions, model abuse, data leakage, and supply chain risk. • Establish architecture standards for identity, secrets management, data classification, network boundaries, logging, model access, and third party AI service consumption. • Design guardrails and security patterns for input filtering, grounding controls, policy enforcement, human in the loop checkpoints, and runtime monitoring. • Partner with engineering teams to embed security requirements into AI platforms, developer workflows, CI/CD pipelines, and production deployment patterns. • Drive evaluation of AI security tooling, including posture management, red teaming, model and prompt testing, observability, and policy enforcement capabilities. • Create implementation guidance, reference patterns, and design reviews that help teams deliver secure AI features consistently at scale. • Support incident readiness by defining telemetry, detections, escalation points, and response considerations for AI specific security events. • Influence enterprise AI governance by connecting architecture decisions to risk, compliance, privacy, and acceptable use expectations. Minimum Job Requirements Education Bachelors degree in Cybersecurity, Computer Science, Information Security, Software Engineering, Computer Engineering, or a related technical field. Equivalent practical experience is also valued. Certification / License None required. Work Experience • 7+ years of experience in cybersecurity, software security, cloud security, or security architecture roles. • 2+ years of direct experience designing or securing AI, ML, LLM, or data intensive platforms in production environments. • Hands on experience with cloud native architecture patterns in AWS, Azure, or GCP, including IAM, logging, networking, encryption, and secrets management. • Experience performing threat modeling, architecture review, and security design for modern applications, APIs, and distributed systems. • Ability to translate security risk into practical technical requirements and influence engineers, architects, and platform owners. Knowledge / Skills / Abilities • Strong understanding of AI system components, including models, prompts, retrieval layers, agents, plugins, orchestration frameworks, and data pipelines. • Working knowledge of AI security risks such as prompt injection, insecure tool use, excessive autonomy, training data exposure, sensitive data leakage, and model supply chain concerns. • Strong grasp of core security architecture domains, including identity, cryptography, application security, cloud security, logging, monitoring, and incident response. • Clear communicator who can work across engineering, product, privacy, legal, and executive stakeholders. Work Experience Experience securing AI platforms built on services such as AWS Bedrock, Anthropic, Azure OpenAI, Microsoft Azure AI, Google Vertex AI, or equivalent. • Experience with AI security testing, red teaming, prompt attack evaluation, or model and agent assessment techniques. • Familiarity with orchestration and agent frameworks • Experience implementing policy, observability, and runtime control layers around AI systems and external tools. • Exposure to governance frameworks such as NIST AI RMF, OWASP Top 10 for LLM Applications, MITRE ATLAS, or similar guidance. Knowledge / Skills / Abilities Ability to balance secure by design principles with speed of delivery, and to convert emerging AI risks into practical standards, reusable patterns, and engineering guidance.