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, LLM-powered SOAR, telemetry pipeline architecture, and technical leadership across the security programme. The role is full-time and based in Bangalore, India.

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 for threat hunting, vulnerability management, configuration audit, and incident response, with tamper-proof observability of reasoning traces, t
• 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 overall 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 e
• 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), with experience 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.

Skills

Education

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