Staff Software Architect

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. Overall experience of 8+ years in software engineering, including significant hands-on software development experience. 4+ 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. Bachelor's or Master's 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.