Senior AI Security & MLSecOps Architect

About the role

At o9, the Senior AI Security & MLSecOps Architect in Bangalore, India, leads AI-native security for the GenAI platform and agentic AI ecosystem while building ML-driven security operations. Day-to-day work includes designing AI agent security architecture, RAG and prompt security controls, AIBOM pipelines, threat detection and anomaly modelling, autonomous security agents, and scalable telemetry pipelines across 500+ customer environments. The role also sets architecture standards, drives research, and mentors engineers bridging ML and security.

What you’ll do

• Own the security architecture for o9's GenAI platform, ensuring every AI agent, model, and integration is governed, auditable, and stoppable.
• Design and enforce agent identity controls, permission scoping, and behavioural baselining for production AI agents, and build UEBA-style models that detect agent deviations and trigger automated cont
• Architect the AI Bill of Materials pipeline for model provenance verification, hash integrity, dependency scanning, and supply chain trust, ensuring no model reaches production without a signed invent
• Design security controls for RAG pipelines including source allowlisting, tenant isolation, PII scrubbing, indirect prompt injection detection, and embedding anomaly monitoring.
• Build, fine-tune, and deploy ML models that detect anomalous patterns, novel attack variations, and stealthy TTPs mapped to MITRE ATT&CK, including predictive weak-point analysis.
• Architect and build the autonomous security agent layer—threat-hunting, vulnerability-management, configuration-audit, and incident-response agents—with tamper-proof, forensically auditable observabil
• Evolve SOAR playbooks from rule-based automation to ML-driven, context-aware orchestration with LLM-based enrichment, targeting false-positive rates below 20% while preserving detection fidelity.
• Design scalable, real-time telemetry pipelines that ingest, normalise, and correlate high-velocity security telemetry from EDR, WAF/ZTNA, PAM, cloud environments, and DevOps toolchains into a unified

What you’ll bring

• 10+ years progressive hands-on experience in cybersecurity engineering, AI security, or security data science, with a foundational identity as a software engineer who developed deep security expertise
• 3+ years in AI security or MLSecOps, including AI platform security (agent governance, model risk, RAG security) or building and operationalising ML models for security use cases at production scale.
• Demonstrated experience building autonomous agents using frameworks such as CrewAI, LangChain, LangGraph, or equivalent, including tool-use, multi-agent orchestration, and agent observability.
• Hands-on experience with enterprise security platforms such as EDR, NG-SIEM, SOAR, WAF/ZTNA, PAM, or equivalents at comparable scale.
• Deep experience securing large-scale containerised environments across AWS, Azure, and GCP, with exposure to multi-tenant SaaS architectures.
• Python for ML model development, pipeline authoring, and security automation, plus streaming and batch platforms such as Spark, Kafka, Elasticsearch, or equivalent.
• ML frameworks (scikit-learn, XGBoost, PyTorch) and LLM/agent orchestration (LangChain, CrewAI), including integrating LLM APIs into security workflows via prompt engineering and RAG pipelines.
• Bachelor's in Computer Science, Software Engineering, or related discipline required; Master's in CS, Data Science, AI, or Cybersecurity highly preferred.

Nice to have

• Relevant certifications are a strong plus, including cloud security specialties (AWS/Azure/GCP), CISM, MITRE ATT&CK Defender, or SANS AI/ML security courses.
• Experience with ML model lifecycle management (MLflow or equivalent), experiment tracking, model drift detection, and production monitoring for security ML models.

Skills

Education

Bachelor's in Computer Science, Software Engineering, or related discipline required; Master's in CS, Data Science, AI, or Cybersecurity highly preferred.