Senior Cyber Security Engineer
6 - 10 years
Chicago, IL
Contract
Onsite
1 month ago
About the role
Required Qualifications:
• Security Architecture & Engineering:• 8+ years of experience in cybersecurity, with at least 3 years focused on security architecture or engineering.
• Demonstrated ability to design end-to-end security architectures for cloud-native and hybrid enterprise environments
• Strong working knowledge of network security, application security, data protection, and zero-trust principles
• Identity, Authentication & Access Management (IAA/IAM):• Hands-on experience designing and implementing IAM solutions in enterprise environments (e.g., Entra ID / Azure AD, Okta, Ping, AWS IAM)
• Deep understanding of authentication and authorization protocols: OAuth 2.0, OIDC, SAML, SCIM, and token-based flows (including on-behalf-of and client credential grants)
• Experience with service identity management, managed identities, workload identity federation, and privileged access governance for non-human actors
• AI / Machine Learning Security:• 1-3 years of demonstrated experience working with AI/ML systems in a security, governance, or engineering capacity. This is calibrated to the maturity of the enterprise AI space—we recognize the field is young and value depth of engagement over length of tenure
• Practical understanding of LLM deployment patterns, agentic AI frameworks (e.g., LangChain, LangGraph), and the security risks they introduce
• Familiarity with AI-specific threat vectors: prompt injection, training data poisoning, model inversion, tool/plugin abuse, and supply chain risks in model and connector ecosystems
• Exposure to AI governance frameworks and standards: NIST AI RMF, EU AI Act, OWASP AI Top 10, MITRE ATLAS
• Communication & Stakeholder Engagement:• Excellent written and verbal communication skills, with a proven ability to translate complex technical security concepts into business-relevant language for executive and non-technical audiences
• Experience authoring formal security documentation: architecture decision records, risk assessments, implementation guides, and policy documents
• Demonstrated ability to influence cross-functional teams, facilitate architecture review boards, and present security recommendations with clarity and confidence
Preferred Qualifications:• Experience in financial services, healthcare, or other heavily regulated industries with multi-jurisdictional compliance requirements (e.g., SOX, GDPR, MiFID II, SR 11-7)
• Hands-on experience with Microsoft Azure and M365 security ecosystems, including Entra ID, Azure AI Foundry, Copilot Studio, Defender for Cloud, and Purview
• Familiarity with API gateway security patterns for AI services (e.g., Azure APIM, Kong, Cloudflare AI Gateway)
• Knowledge of model security scanning, container security for ML workloads, and secure MLOps pipeline design
• Experience evaluating or implementing Model Context Protocol (MCP) security controls
• Background in contributing to security communities of practice, mentoring junior engineers, or publishing security research
AI Security Engineer Summary:• We are seeking an experienced AI Security Engineer to lead the design, assessment, and governance of security controls for AI and machine learning systems across the enterprise
• This role sits at the intersection of cybersecurity architecture, identity and access management (IAM), and emerging AI/ML technologies
• You will be responsible for ensuring that AI workloads—including large language models, agentic frameworks, and ML pipelines—are deployed securely within a complex, regulated environment
• The ideal candidate combines deep security architecture expertise with practical, hands-on experience in AI systems
• Given that enterprise AI adoption is still a rapidly evolving discipline, we value demonstrated engagement with AI security concepts and tooling proportional to the maturity of the field
Job Responsibilities:• Design and implement security architectures for AI/ML platforms, including model hosting environments, inference endpoints, training pipelines, and agentic AI systems
• Develop and enforce identity, authentication, and authorization (IAA) frameworks for AI workloads, ensuring least-privilege access, service identity governance, and secure token flows (e.g., OAuth 2.0, OBO, managed identities)
• Lead threat modeling and risk assessments for AI deployments, leveraging frameworks such as OWASP AI Top 10, MITRE ATLAS, and NIST AI RMF
• Evaluate and harden AI supply chain components, including model registries, MCP servers, API gateways, and third-party integrations
• Define IAM policies and role-based access controls for AI development and production environments across cloud platforms (Azure, AWS, or GCP)
• Collaborate with data science, platform engineering, and compliance teams to embed security guardrails into the AI development lifecycle without impeding velocity
• Author security architecture documents, threat and risk assessments, tactical exception requests, and developer implementation guides for AI-related initiatives
• Monitor the evolving AI threat landscape—including prompt injection, tool poisoning, data exfiltration via agentic workflows, and model manipulation—and translate findings into actionable controls
• Present technical security findings, risk postures, and architectural recommendations to senior leadership, governance boards, and cross-functional stakeholders in clear, accessible language
• Contribute to enterprise security standards and policies governing AI adoption, including acceptable use, data handling, and model governance
• Security Architecture & Engineering:• 8+ years of experience in cybersecurity, with at least 3 years focused on security architecture or engineering.
• Demonstrated ability to design end-to-end security architectures for cloud-native and hybrid enterprise environments
• Strong working knowledge of network security, application security, data protection, and zero-trust principles
• Identity, Authentication & Access Management (IAA/IAM):• Hands-on experience designing and implementing IAM solutions in enterprise environments (e.g., Entra ID / Azure AD, Okta, Ping, AWS IAM)
• Deep understanding of authentication and authorization protocols: OAuth 2.0, OIDC, SAML, SCIM, and token-based flows (including on-behalf-of and client credential grants)
• Experience with service identity management, managed identities, workload identity federation, and privileged access governance for non-human actors
• AI / Machine Learning Security:• 1-3 years of demonstrated experience working with AI/ML systems in a security, governance, or engineering capacity. This is calibrated to the maturity of the enterprise AI space—we recognize the field is young and value depth of engagement over length of tenure
• Practical understanding of LLM deployment patterns, agentic AI frameworks (e.g., LangChain, LangGraph), and the security risks they introduce
• Familiarity with AI-specific threat vectors: prompt injection, training data poisoning, model inversion, tool/plugin abuse, and supply chain risks in model and connector ecosystems
• Exposure to AI governance frameworks and standards: NIST AI RMF, EU AI Act, OWASP AI Top 10, MITRE ATLAS
• Communication & Stakeholder Engagement:• Excellent written and verbal communication skills, with a proven ability to translate complex technical security concepts into business-relevant language for executive and non-technical audiences
• Experience authoring formal security documentation: architecture decision records, risk assessments, implementation guides, and policy documents
• Demonstrated ability to influence cross-functional teams, facilitate architecture review boards, and present security recommendations with clarity and confidence
Preferred Qualifications:• Experience in financial services, healthcare, or other heavily regulated industries with multi-jurisdictional compliance requirements (e.g., SOX, GDPR, MiFID II, SR 11-7)
• Hands-on experience with Microsoft Azure and M365 security ecosystems, including Entra ID, Azure AI Foundry, Copilot Studio, Defender for Cloud, and Purview
• Familiarity with API gateway security patterns for AI services (e.g., Azure APIM, Kong, Cloudflare AI Gateway)
• Knowledge of model security scanning, container security for ML workloads, and secure MLOps pipeline design
• Experience evaluating or implementing Model Context Protocol (MCP) security controls
• Background in contributing to security communities of practice, mentoring junior engineers, or publishing security research
AI Security Engineer Summary:• We are seeking an experienced AI Security Engineer to lead the design, assessment, and governance of security controls for AI and machine learning systems across the enterprise
• This role sits at the intersection of cybersecurity architecture, identity and access management (IAM), and emerging AI/ML technologies
• You will be responsible for ensuring that AI workloads—including large language models, agentic frameworks, and ML pipelines—are deployed securely within a complex, regulated environment
• The ideal candidate combines deep security architecture expertise with practical, hands-on experience in AI systems
• Given that enterprise AI adoption is still a rapidly evolving discipline, we value demonstrated engagement with AI security concepts and tooling proportional to the maturity of the field
Job Responsibilities:• Design and implement security architectures for AI/ML platforms, including model hosting environments, inference endpoints, training pipelines, and agentic AI systems
• Develop and enforce identity, authentication, and authorization (IAA) frameworks for AI workloads, ensuring least-privilege access, service identity governance, and secure token flows (e.g., OAuth 2.0, OBO, managed identities)
• Lead threat modeling and risk assessments for AI deployments, leveraging frameworks such as OWASP AI Top 10, MITRE ATLAS, and NIST AI RMF
• Evaluate and harden AI supply chain components, including model registries, MCP servers, API gateways, and third-party integrations
• Define IAM policies and role-based access controls for AI development and production environments across cloud platforms (Azure, AWS, or GCP)
• Collaborate with data science, platform engineering, and compliance teams to embed security guardrails into the AI development lifecycle without impeding velocity
• Author security architecture documents, threat and risk assessments, tactical exception requests, and developer implementation guides for AI-related initiatives
• Monitor the evolving AI threat landscape—including prompt injection, tool poisoning, data exfiltration via agentic workflows, and model manipulation—and translate findings into actionable controls
• Present technical security findings, risk postures, and architectural recommendations to senior leadership, governance boards, and cross-functional stakeholders in clear, accessible language
• Contribute to enterprise security standards and policies governing AI adoption, including acceptable use, data handling, and model governance
Skills
Information TechnologyIT Services and IT Consulting