SecOps Architect I
On-siteBengaluru, Karnataka, India
Job Summary
Secure o9's GenAI platform and agentic AI ecosystem by designing agent identity controls, enforcing permission scoping, and building UEBA models to detect deviations and trigger automated containment. Architect AI Bill of Materials pipelines for model provenance verification and supply chain trust, while securing RAG pipelines through source allowlisting, tenant isolation, and indirect prompt injection detection. Build ML models and autonomous agents to convert raw security telemetry into predictive defenses, including threat-hunting agents, vulnerability-management agents, and LLM-powered SOAR orchestration to reduce false positives. Design scalable, real-time pipelines ingesting high-velocity data from EDR, WAF, and cloud environments into a unified security data model, embedding security telemetry and policy-as-code into CI/CD for shift-left DevSecOps. Serve as technical authority for AI security standards across the organization, mentoring engineers in ML/AI fundamentals while evaluating emerging techniques for production capabilities.
Required Qualifications
- 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 expertise
- 3+ Years AI Security / MLSecOps: Proven experience in AI platform security (agent governance, model risk, RAG security) or building and operationalising ML models for security use cases at production scale
- AI Agent Engineering: Demonstrated experience building autonomous agents using frameworks such as CrewAI, LangChain, LangGraph, or equivalent — including tool-use, multi-agent orchestration, and agent observability
- Security Platform Depth: Hands-on experience with enterprise security platforms — EDR, NG-SIEM, SOAR, WAF/ZTNA, PAM, or equivalents at comparable scale
- Cloud & Kubernetes: Deep experience securing large-scale containerised environments across AWS, Azure, and GCP — with exposure to multi-tenant SaaS architectures
- Languages & Data Engineering: Python for ML model development, pipeline authoring, and security automation
- Streaming and batch platforms (Spark, Kafka, Elasticsearch, or equivalent)
- ML Frameworks & LLM Integration: ML frameworks (scikit-learn, XGBoost, PyTorch) and LLM/agent orchestration (LangChain, CrewAI)
- Experience integrating LLM APIs into security workflows via prompt engineering and RAG pipelines
- Security Frameworks: MITRE ATT&CK, MITRE ATLAS, OWASP Top 10 for LLM Applications, NIST AI RMF, ISO 42001, ISO 27001
- Observability & MLOps: ML model lifecycle management (MLflow or equivalent), experiment tracking, model drift detection, and production monitoring for security ML models
- Bachelor's in Computer Science, Software Engineering, or related discipline required
Desired Qualifications
- Master's in CS, Data Science, AI, or Cybersecurity highly preferred
- Relevant certifications are a strong plus: cloud security specialties (AWS/Azure/GCP), CISM, MITRE ATT&CK Defender, or SANS AI/ML security courses
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