At o9 Solutions, our mission is to transform enterprise decision-making through an AI-first platform.

Global organizations including Google, PepsiCo, Walmart, T-Mobile, AB InBev, and Starbucks trust o9 to optimize their planning and supply chains.

About the Role :

We are looking for a Senior AI Security & MLSecOps Architect to lead the next generation of AI-native security at o9.

This role has two key missions :

- Secure o9's GenAI and agentic AI ecosystem against emerging AI-specific threats.

- Build AI/ML-driven security capabilities that improve threat detection, prediction, and automated response across 500+ customer environments.

You will work across AI security architecture, agent security, RAG security, MLSecOps, cloud security, detection engineering, and autonomous security operations.

What You'll Do :

AI Security & Governance :

- Define security architecture for GenAI, LLMs, AI agents, RAG pipelines, and MCP integrations.

- Implement agent identity, authorization, permission scoping, behavioral monitoring, and automated containment.

- Establish AI model/SBOM governance, model provenance, dependency security, and supply-chain controls.

- Design controls for prompt injection, data leakage, tenant isolation, PII protection, and AI-specific threats.

- Align AI security practices with NIST AI RMF, MITRE ATLAS, OWASP LLM Top 10, ISO 42001, and ISO 27001.

AI/ML for Security Operations :

- Build ML-driven threat detection, anomaly detection, identity risk scoring, and predictive security capabilities.

- Architect autonomous security agents for threat hunting, vulnerability management, configuration auditing, and incident response.

- Integrate LLMs into SOAR, security triage, investigation, and response workflows.

- Use security telemetry to identify emerging attack patterns and improve detection accuracy.

Security Platform & Cloud :

- Design scalable security telemetry pipelines across EDR, SIEM, SOAR, WAF/ZTNA, PAM, cloud, and DevOps environments.

- Develop cross-platform detection and correlation capabilities.

- Embed security and observability requirements into CI/CD through DevSecOps and policy-as-code.

- Secure large-scale AWS, Azure, and GCP environments, including Kubernetes and multi-tenant SaaS architectures.

Technical Leadership :

- Serve as the technical authority for AI Security and MLSecOps.

- Define architecture standards and security engineering best practices.

- Evaluate emerging AI security technologies and translate research into production capabilities.

- Mentor security and ML engineers across both disciplines.

What You'll Bring :

- 10+ years of experience in cybersecurity engineering, software engineering, security architecture, or security data science.

- 3+ years of hands-on experience in AI Security, MLSecOps, or production ML security use cases.

- Strong understanding of GenAI/LLM security, AI agents, RAG, prompt injection, model risk, and AI governance.

- Experience with AI agent frameworks such as LangChain, LangGraph, CrewAI, or equivalent.

- Strong experience with enterprise security platforms such as EDR, SIEM, SOAR, WAF/ZTNA, or PAM.

- Deep cloud and Kubernetes security experience across AWS, Azure, and/or GCP.

- Strong Python experience for security automation, ML development, or data pipelines.

- Experience with ML frameworks such as scikit-learn, XGBoost, or PyTorch.

- Understanding of MITRE ATT&CK, MITRE ATLAS, OWASP LLM Top 10, NIST AI RMF, ISO 42001, and ISO 27001.

- Experience with MLOps/model lifecycle management is a plus.

- Strong architecture, communication, and technical leadership skills.

Education & Certifications :

- Bachelor's degree in Computer Science, Software Engineering, Cybersecurity, AI, or a related field.

- Master's degree is a plus.

- Cloud security, CISM, SANS, or other relevant security certifications are a plus.

Why o9?

o9 Solutions is a rapidly growing technology company with a global presence and a $3.7B valuation.

Our security organization protects 500+ customer environments across 60+ countries, with large-scale cloud, Kubernetes, and security infrastructure.

This is an opportunity to define how AI security is built and operated at enterprise scale, rather than simply maintaining existing security controls.