AI Security Engineer
Job in
Denver, Denver County, Colorado, 80285, USA
Listed on 2026-10-04
Listing for:
Cybersecurity Jobs
Full Time
position Listed on 2026-10-04
Job specializations:
-
IT/Tech
Cybersecurity, AI Engineer (Applied/Software), Information Security & Data Protection
Job Description & How to Apply Below
Janus Henderson is seeking a hands-on AI Security Engineer to help secure AI-enabled applications, models, agents, and the platforms that support them across the full lifecycle. Reporting into the Office of the CISO, this role acts as an AI security design authority for agentic systems and related capabilities, bringing threat modeling, guardrails, testing, and risk-based assurance to enterprise-scale deployments.
Working onsite in Denver, CO within the Office of the CISO
, you will help define what secure-by-design looks like for AI at the firm, ensuring protections are implemented as code and supported by measurable security outcomes.
- Serve as the security design authority for AI systems by leading threat modelling
, architecture review
, and risk assessment for agentic applications, the model gateway, and initiatives including Accio and Nexus
, applying frameworks such as STRIDE
, PASTA
, MITRE ATT&CK
, and MITRE ATLAS as appropriate. - Define and evolve AI security standards
, guardrails
, governance controls
, and secure-by-design patterns aligned to enterprise requirements and the firm’s risk appetite, co-designing reusable guardrails with the Principal AI Architect and collaborating with the AI Governance Implementation Lead for platform landing. - Design identity, entitlement, and secrets patterns for non-human identities including agents, tools,
MCP servers
, connectors, and service principals, covering scoped permissions, credential lifetime, rotation, and least privilege across multi-hop requests with the Senior AI Platform Engineer and enterprise IAM. - Establish trust boundaries and data-egress controls for the AI estate, including what external model providers may receive, and work with data protection stakeholders on classification,
DLP enforcement
, privacy, and data governance obligations. - Run adversarial testing and AI red teaming for models, prompts, agents, and tool chains, including prompt injection and indirect injection, model manipulation and hijacking, excessive agency, function-call abuse, data leakage from LLM outputs, and privilege escalation between agents.
- Build detections, security analytics, and telemetry for AI-specific abuse such as anomalous tool invocation, credential misuse by agents, unusual data access, and exfiltration through model responses, integrating findings into the firm’s monitoring estate.
- Support security incident response for AI systems through triage, containment, and forensics across prompts, tool calls, and agent decisions, then feed lessons learned back into platform defaults and evaluation suites.
- Own security testing across the AI delivery lifecycle, including static analysis
, dependency and container scanning
, secrets detection
, and security gates in CI/CD to surface issues before release. - Co-own risk-based security gates for onboarding new AI products, model providers, versions, and platform features, including security due diligence and risk assessment of vendors, platforms, and models (provenance, open-source components, tenancy and data-use terms, security advisories), and testing platform updates before rollout.
- Help determine which Copilot features are released and to whom, ensuring capability release aligns with required controls being evidenced; review Forward Deployed Engineering solutions to help prevent production access without an approved security position, and set guardrails for citizen developer patterns and Copilot Studio builders with AI Enablement.
- Support Risk and Internal Audit with security evidence for AI, including the Infosec control approval and risk-acceptance position, and elevate when residual risk exceeds appetite.
- Identify opportunities to apply AI and automation across security operations, engineering, assurance, and governance, building automation with code, APIs, scripting, orchestration platforms, or low-code technologies to automate response workflows, enrich incident data, assist triage, and enable risk-based vulnerability management.
- Define and report KPIs
, KRIs
, dashboards, and reporting that demonstrate risk reduction, control effectiveness, and operational improvement. - Mentor security engineers on AI security and build AI literacy across Infosec to reduce single-point-of-knowledge dependencies.
- Strong cybersecurity experience across security engineering, application security, product security, cloud security, or security architecture, with a hands-on engineering background and security protections that…
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