Software Security Architect (AI Systems)
Software Security Architect (AI Systems)
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Job Description
We are seeking a highly experienced Senior Software Security Architect to lead the design and enforcement of robust security architectures across our AI and machine learning platforms. This role focuses on ensuring the secure design implementation and operation of AI systems including agentic AI large language model (LLM) integrations and machine learning pipelineswhile aligning with modern DevSecOps and enterprise compliance standards.
Key Responsibilities:
Architect Secure AI Systems: Design end-to-end security for AI/ML systems including model training pipelines data ingestion workflows inference APIs and agentic AI orchestration (e.g. using n8n LangChain Azure ML etc.).
Threat Modeling & Risk Assessment: Conduct in-depth threat modeling and risk assessments for AI applications including adversarial attacks model poisoning data leakage prompt injection and misuse of LLMs.
Policy & Governance: Establish and enforce AI-specific security policies including Model Context Protocol (MCP) integration audit trails data access controls and responsible AI guidelines.
Secure Code & Infrastructure: Guide engineering teams on secure development practices for AI workloads running on cloud-native infrastructure (e.g. Kubernetes Azure AWS GCP) and integrating with vector databases and APIs.
Data Privacy & Compliance: Ensure AI systems comply with regulatory and industry standards (GDPR NIST ISO 27001 etc.) with a focus on data provenance lineage and user privacy.
Tooling & Automation: Evaluate and implement security automation tools (e.g. SAST/DAST SBOM scanning model validation AI-specific security tools) within CI/CD pipelines.
Incident Response & Monitoring: Define AI-specific observability and response strategies for misuse model drift unauthorized access and data exfiltration.
Cross-Team Leadership: Collaborate with platform engineers AI/ML teams enterprise architects and legal/compliance stakeholders to drive secure-by-design principles across the AI ecosystem.
Required Qualifications:
Overall experience of 12 years in software engineering including significant hands-on development.
8 years of experience in software security architecture with at least 2 years focused on AI/ML platforms or services.
Deep understanding of software and cloud security principles including identity and access management encryption secrets management and network segmentation.
Familiarity with AI security risks model lifecycle management and ML pipeline security (e.g. MLflow TensorFlow Extended Azure ML).
Hands-on experience with securing LLM-based applications API endpoints prompt engineering and protecting model endpoints.
Strong coding and architecture skills in Python TypeScript or Java and experience with secure CI/CD practices (GitHub Actions Azure DevOps etc.).
Experience with infrastructure-as-code (Terraform Bicep Pulumi) and Kubernetes security best practices.
Excellent communication and documentation skills with the ability to influence technical and executive audiences.
Bachelors or Masters degree in Computer Science Engineering or related field.
Preferred Qualifications:
Certifications: CISSP CCSP OSWE or AI-specific certifications (e.g. Microsoft AI-102 NVIDIA AI).
Experience with agentic AI frameworks LangChain Semantic Kernel or OpenAI integrations.
Prior experience implementing AI governance frameworks or responsible AI initiatives.
Employment Type
Full-Time
