Cybersecurity AI Architect

Overview

Define and govern secure architecture for LLM, RAG, agentic and other AI-enabled systems across cloud and enterprise environments, embedding security controls into AI platforms and engineering workflows.

What you'll do

• 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

What you'll need

• Bachelor's degree in Cybersecurity, Computer Science, Information Security, Software Engineering, Computer Engineering, or a related technical field
• 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
• 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
• Ability to translate security risk into practical technical requirements and influence engineers, architects, and platform owners

Nice to have

• Bachelor's or Master's degree in Cybersecurity, Computer Science, Software Engineering, Data Science, Machine Learning, or a related technical discipline
• Cloud security, security architecture, or AI/ML related certifications
• 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
• 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

Details

• Location: Pune, India