Note: The job is a remote job and is open to candidates in USA. RIT Solutions, Inc. is seeking an experienced AI Security Architect to support the secure design, development, and deployment of AI/ML and Generative AI solutions across the enterprise. This role involves collaborating with various teams to define security requirements, establish security controls, and ensure compliance and resilience in AI adoption.
Responsibilities
- Collaborate with AI/ML engineering, data engineering, platform, and application teams to gather and analyze AI security requirements (data sourcing, model training, inference, integrations, and operational workflows)
- Define and document secure reference architectures for AI/ML and GenAI workloads across cloud and enterprise environments, including patterns for model hosting, RAG, agents, API exposure, and third-party model consumption
- Perform threat modeling for AI systems (training and inference) including risks such as prompt injection, data leakage, model inversion/extraction, insecure plugins/tools, and supply chain risks
- Establish security controls for data protection across the AI lifecycle: dataset governance, data minimization, labeling/classification alignment, encryption, key management, secrets handling, and secure data access patterns
- Define requirements and guardrails for GenAI safety and misuse prevention, including content filtering, prompt/response logging strategy, abuse detection, and safe tool execution
- Partner with IAM teams to implement least-privilege access for AI platforms, model endpoints, feature stores/vector databases, and supporting pipelines (service-to-service auth, token handling, RBAC/ABAC)
- Review and approve AI solution designs, ensuring alignment with security policies, regulatory expectations, and enterprise standards (secure SDLC/DevSecOps)
- Define requirements for model governance and assurance (model provenance, versioning, artifact integrity, evaluation/validation controls, documentation, and auditability)
- Work with security operations to integrate AI platforms into monitoring and incident response, including logging requirements, detection use cases, and response playbooks for AI-specific incidents
- Contribute to security standards and best practices: "when to use enterprise controls vs platform-native controls,” secure configuration baselines, and reusable implementation guidance
Skills
- 5+ years of experience in security architecture/engineering, with demonstrated experience securing cloud-native applications and APIs (AI/ML experience required)
- Strong understanding of AI/ML and GenAI solution patterns (model training vs inference, RAG, vector databases, agents/tool use, model endpoints)
- Proven ability to translate business and technical requirements into actionable security controls and architecture decisions
- Experience with cloud security fundamentals (network segmentation, IAM, encryption, secrets management, logging/monitoring) in at least one major cloud provider (AWS/Azure/GCP)
- Hands-on experience with application security and API security (authN/authZ, OWASP Top 10, secure SDLC, threat modeling)
- Familiarity with AI/ML security risks and mitigations (prompt injection, data leakage, jailbreaks, model theft, poisoning, insecure dependencies)
- Strong communication skills—able to document and present complex technical topics to engineering and leadership audiences
- Ability to work independently and drive results in a fast-paced, multi-team environment
- Experience securing enterprise GenAI platforms and services (e.g., Azure OpenAI/OpenAI APIs, AWS Bedrock, SageMaker, Vertex AI) and/or popular frameworks (e.g., LangChain/LlamaIndex) and vector databases
- Experience with governance and compliance in regulated environments (financial services a plus)
- Familiarity with security evaluation/testing approaches for AI systems (red teaming, prompt testing, model risk assessments)
- Relevant certifications (examples): AWS/Azure/GCP security certifications, CISSP/CCSP, or equivalent experience
- Experience with policy-as-code and automation (e.g., Terraform + scanning, CI/CD security controls)
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