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202 results (9 new)

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AI Security Architect

VDart, Inc.

Fort Worth, Texas • Today

Easy Apply

Contract, Third Party

Depends on Experience

AI Security Architect

ARK Infotech Spectrum

Fort Worth, Texas • Yesterday

Easy Apply

Third Party, Contract

Depends on Experience

AI Security Architect/Partner

Tradeweb Markets LLC

Remote or New York, New York • Today

Full-time

Sr AI Information Security Architect

Mercury Insurance

Remote • Today

Full-time

USD 128,136.00 - 252,194.00 per year

Sr Enterprise AI Security Architect

WEX Inc

Remote • Today

Full-time

USD 143,400.00 - 189,100.00 per year

Senior Cloud Security Architect

Idexcel Inc.

Remote • 6d ago

Easy Apply

Third Party, Contract

$60+

Senior Security Engineer (AI)

Peterson Technology Partners

Irving, Texas • Yesterday

Third Party, Contract

$70 - $80

AI Security Engineer

Kforce Technology Staffing

Irving, Texas • Today

Contract

$57 - $62 hourly

AI & Offensive Security Analyst - Vice President

Citi

Irving, Texas • Today

Full-time

USD 125,760.00 - 188,640.00 per year

Principal AI Security Platform & Harness Engineer

DTCC

Hybrid in Coppell, Texas • Today

Full-time

Principal AI Security Automation Engineer

DTCC

Hybrid in Coppell, Texas • Today

Full-time

AI Architect

Cambium Learning Group, Inc.

Remote or Dallas, Texas • Today

Full-time

AI Architect

NTT DATA, Inc.

Remote or Dallas, Texas • Today

Full-time

USD 80.00 - 90.00 per hour

AI Architect

Purplejack Technologies LLC

Irving, Texas • Today

Easy Apply

Contract

Principal Software Architect - AI & Security

Mainspring Energy

Remote or Menlo Park, California • Today

Full-time

USD 227,500.00 - 280,000.00 per year

Security Architect

Vaco by Highspring

Remote • 6d ago

Easy Apply

Contract

$130

Senior AI Security Engineer

GDH

Remote • 5d ago

Easy Apply

Third Party, Contract

Depends on Experience

Security NLP Architect

I-Link Solutions

Remote • Yesterday

Easy Apply

Contract, Third Party

Depends on Experience

Information Security Architect

BambooHR

Remote or Utah • Today

Full-time

AI/ML Architect - Forward Deployed Engineer Associate Manager - Reinvention Centers

Accenture LLP

Irving, Texas • Today

Full-time

USD 73,800.00 per year

Senior CyberSecurity Architect-AI/ML

SPECTRAFORCE TECHNOLOGIES Inc.

Remote • 30+d ago

Easy Apply

Contract

90 - 100

Security Architect

Infinite Computer Solutions (ICS)

Remote • 8d ago

Easy Apply

Full-time

$80,000 - $100,000

Security Architect

Nasscomm, Inc.

Remote • 8d ago

Easy Apply

Third Party, Contract

Depends on Experience

Security Architect

InterSources Inc.

Remote • 21d ago

Easy Apply

Contract

Depends on Experience

IT Security Architect

Conduent

Remote or Atlanta, Georgia • Today

Full-time

USD 130,000.00 - 160,000.00 per year

AI Security Engineer - Mid-Atlantic region (Remote in VA, MD, PA, NC, DE, NJ, or DC)

GuidePoint Security

Remote • Yesterday

Full-time

AI Governance Security Engineer

Zoom Video Communications

Remote • Today

Full-time

USD 98,900.00 - 228,700.00 per year

Principal Engineer - AI Security

Thrivent Financial for Lutherans

Remote or Minnesota • Today

Full-time

USD 161,436.00 - 218,415.00 per year

AI Security Systems Associate Director

NTT DATA Americas, Inc

Remote • 2d ago

Easy Apply

Contract

$90 - $100

AI Security Engineer

Cyrino

Remote • 16d ago

Easy Apply

Contract

Depends on Experience

VDart, Inc.

Job Details

Skills

AI
SOC
Security
MCP

Summary

Job Title: AI Security Architect

Location: Fort Worth, TX(Onsite)

Type: Contract

We are seeking an experienced and highly skilled AI Security hands-on, highly technical architect responsible for defining security architecture and implementing robust security controls for our AI/ML systems and their underlying platforms and will serve as the team’s technical mentor and architecture authority, driving secure-by-design patterns across the AI/ML lifecycle (data, training, evaluation, deployment, and production monitoring) and proactively mitigating AI-specific threats such as model integrity risks, data poisoning, adversarial attacks, prompt injection, model extraction, and inference-time abuse.
Lead technically, set standards, and guide engineers day-to-day through architecture, reviews, and delivery.
Ensures AI systems are secure, compliant, and resilient by implementing data protection, threat detection, guardrails, and ongoing risk monitoring across the AI lifecycle.

Platform & Enablement Roles

AI Platform Admin (M365, copilot Studio) Manages AI platforms and environments, including access provisioning, governance controls, and policy enforcement (e.g., DLP, security, and compliance).
AI Reusable Utility Develops reusable components (e.g., prompts, connectors, APIs, templates) to accelerate AI solution delivery and promote standardization across use cases.
AI Common Infrastructure, Framework & Observability Architect (AWS and Azure) Designs and maintains the foundational AI infrastructure, frameworks, and observability capabilities (telemetry, monitoring, metrics) required for scalable, reliable, and governed AI operations.

Core Responsibilities

Agent Security

Non-Human Identity & Access: Define strict Role-Based Access Control (RBAC) and least-privilege models for AI agents using identity systems (e.g., Entra Agent ID).
Guardrails & Sandboxing: Design runtime environments with restricted permissions to prevent manipulated agents from accessing unauthorized APIs, data sources, or executing malicious toolchains.
Input/Output Protection: Implement defenses against adversarial attacks, prompt injections, jailbreaking, and sensitive data leakage (DLP) across agent workflows.

Observability & Monitoring

Decision Traceability: Architect logging and monitoring standards to map how reasoning agents use data and call APIs, eliminating "black box" decisions.
Model Drift & Integrity: Monitor models and prompt templates in production to detect behavioral drift, anomalies, and poisoning or evasion attacks.

SOC Monitoring & Automation

Autonomous Security (AI SOC): Design LLM-driven and agentic workflows to improve alert triage, contextual correlation, false-positive filtering, and playbook automation.
Incident Response Playbooks: Establish remediation strategies and threat-hunting procedures for AI-specific events (e.g., compromised model artifacts, hallucination-driven exploits).

Compliance Enablement & Governance

Regulatory Alignment: Map AI-specific controls to established standards like the NIST AI RMF, OWASP Top 10 for LLMs, and GDPR.
Audit Readiness: Build audit pipelines that track and explain everything an agent does to satisfy ongoing AI regulatory compliance and governance requirements.

Architecture & Secure-by-Design Leadership

Define and maintain AI security reference architectures for multiple AI deployment patterns, including MCP / Agentic AI and LLM application stacks (RAG, tools/plugins, agents, orchestration).
Establish and evolve security requirements, patterns, and guardrails across the AI/ML SDLC (design → build → run), including secure pipelines and platform controls.
Own AI security architecture decisions across critical domains: identity, secrets, data protection, network controls, tenancy boundaries, logging/telemetry, and isolation for training/inference.
Control Design & Implementation (Hands-on)
Design and deploy controls to ensure model integrity and governance, including RBAC/ABAC for models, feature stores, data sets, registries, and evaluation artifacts.
Build/enable technical mechanisms for provenance, attestation, signing, and approval workflows (where applicable) across datasets, models, prompts, and deployments.
Drive implementation of runtime protections for AI services (abuse prevention, rate limiting, input/output validation, prompt-injection mitigations, model endpoint hardening, and monitoring).
Threat Modeling, Assurance, and Risk Reduction
Conduct and lead AI/ML-specific threat modeling (data poisoning, model evasion, extraction, inversion, supply-chain, prompt attacks), translate findings into actionable backlogs, and drive remediation.
Define and run security design reviews for AI initiatives; provide clear, pragmatic architecture guidance and document exceptions with risk acceptance paths.
Establish AI security testing approaches (adversarial testing, red-teaming enablement, evaluation security, misuse/abuse cases) and integrate into delivery pipelines.
Tooling, Automation, and Operational Enablement
Design and deliver AI security tooling to improve and automate cybersecurity posture (e.g., controls coverage, policy-as-code, detection engineering, vulnerability management integration, incident response playbooks for AI-specific events).
Define logging/monitoring standards and detection use-cases for AI platforms and LLM apps (drift signals, anomalous access, suspicious prompt patterns, exfiltration indicators, policy violations).
Technical Mentorship & Influence (No Line Management)
Act as the team’s technical mentor: coach engineers through designs, implementations, and trade-offs; raise engineering quality via reviews, pairing, and knowledge sharing.
Lead by influence across Data Science, Engineering, Product, Platform, and Cybersecurity—driving alignment without formal authority.
Create internal enablement materials: runbooks, architecture standards, reusable patterns, and reference implementations.

Ideal Qualifications

Experience: 7+ years in cybersecurity architecture with proven experience securing large-scale LLM deployments and multi-agent workflows.
Technical Proficiency: 5+ years of hands-on capability with agent frameworks (e.g., LangChain, LangGraph, AutoGen) and MLOps platforms.
Framework Knowledge: 3 to 5 years of Deep familiarity with model risk management principles and AI security standards
Common Expectation from all the roles:
Compliance with Client’s responsible AI principles and Acceptable Use policy
Adherence to data residency, privacy (GDPR, HIPAA where applicable), and 21 CFR Part 11 controls where in scope
Third-party risk assessment and SOC 2 Type II (or equivalent) certification
Disclosure of subcontractors and offshore delivery locations
Disclosure of model providers, training data practices, and any use of client data for model improvement (opt-out required)

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 10330808
Position Id: 99729-5195-1785956156
Posted 10 hours ago

Company Info

About VDart, Inc.

VDart, headquartered in Atlanta, GA, is a global leader in digital talent solutions and IT staffing, delivering top technology professionals to businesses worldwide. With a strong presence across North America, Europe and Asia, we specialize in helping organizations navigate complex technology landscapes with the right expertise.

Through a strategic, client-focused approach, we have placed over 20,000 professionals across key industries and advanced technology solutions. Whether placing top talent in cutting-edge roles or providing strategic digital workforce solutions, our network of 4,000 specialists across 13 countries is committed to excellence, agility and impact.

Backed by 18 years of industry experience, we go beyond staffing to build long-term partnerships that accelerate digital transformation and drive sustained growth. Whether you need a technology partner to fuel innovation or specialized workforce solutions to maintain a competitive edge, VDart delivers the right people, skills and mindset to create a lasting impact in a digital-first world.

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Security & Agentic AI DevSecOps Engineer

Milpitas, California

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Job Title: Security & Agentic AI DevSecOps Engineer Location: Milpitas, CA Duration: Contract Key Responsibilities AI Security & Agentic Systems (Automation First) Design and implement automated Agentic and AI security controlsfor agentic systems, including: o Automated model access control, inference authorization, and rate limiting o Policy driven prompt/instruction validation and misuse detection o Automated enforcement of agent to agent authentication and authorization

Easy Apply

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VDart, Inc.

Job Details

Skills

AI
SOC
Security
MCP

Summary

Job Title: AI Security Architect

Location: Fort Worth, TX(Onsite)

Type: Contract

We are seeking an experienced and highly skilled AI Security hands-on, highly technical architect responsible for defining security architecture and implementing robust security controls for our AI/ML systems and their underlying platforms and will serve as the team’s technical mentor and architecture authority, driving secure-by-design patterns across the AI/ML lifecycle (data, training, evaluation, deployment, and production monitoring) and proactively mitigating AI-specific threats such as model integrity risks, data poisoning, adversarial attacks, prompt injection, model extraction, and inference-time abuse.
Lead technically, set standards, and guide engineers day-to-day through architecture, reviews, and delivery.
Ensures AI systems are secure, compliant, and resilient by implementing data protection, threat detection, guardrails, and ongoing risk monitoring across the AI lifecycle.

Platform & Enablement Roles

AI Platform Admin (M365, copilot Studio) Manages AI platforms and environments, including access provisioning, governance controls, and policy enforcement (e.g., DLP, security, and compliance).
AI Reusable Utility Develops reusable components (e.g., prompts, connectors, APIs, templates) to accelerate AI solution delivery and promote standardization across use cases.
AI Common Infrastructure, Framework & Observability Architect (AWS and Azure) Designs and maintains the foundational AI infrastructure, frameworks, and observability capabilities (telemetry, monitoring, metrics) required for scalable, reliable, and governed AI operations.

Core Responsibilities

Agent Security

Non-Human Identity & Access: Define strict Role-Based Access Control (RBAC) and least-privilege models for AI agents using identity systems (e.g., Entra Agent ID).
Guardrails & Sandboxing: Design runtime environments with restricted permissions to prevent manipulated agents from accessing unauthorized APIs, data sources, or executing malicious toolchains.
Input/Output Protection: Implement defenses against adversarial attacks, prompt injections, jailbreaking, and sensitive data leakage (DLP) across agent workflows.

Observability & Monitoring

Decision Traceability: Architect logging and monitoring standards to map how reasoning agents use data and call APIs, eliminating "black box" decisions.
Model Drift & Integrity: Monitor models and prompt templates in production to detect behavioral drift, anomalies, and poisoning or evasion attacks.

SOC Monitoring & Automation

Autonomous Security (AI SOC): Design LLM-driven and agentic workflows to improve alert triage, contextual correlation, false-positive filtering, and playbook automation.
Incident Response Playbooks: Establish remediation strategies and threat-hunting procedures for AI-specific events (e.g., compromised model artifacts, hallucination-driven exploits).

Compliance Enablement & Governance

Regulatory Alignment: Map AI-specific controls to established standards like the NIST AI RMF, OWASP Top 10 for LLMs, and GDPR.
Audit Readiness: Build audit pipelines that track and explain everything an agent does to satisfy ongoing AI regulatory compliance and governance requirements.

Architecture & Secure-by-Design Leadership

Define and maintain AI security reference architectures for multiple AI deployment patterns, including MCP / Agentic AI and LLM application stacks (RAG, tools/plugins, agents, orchestration).
Establish and evolve security requirements, patterns, and guardrails across the AI/ML SDLC (design → build → run), including secure pipelines and platform controls.
Own AI security architecture decisions across critical domains: identity, secrets, data protection, network controls, tenancy boundaries, logging/telemetry, and isolation for training/inference.
Control Design & Implementation (Hands-on)
Design and deploy controls to ensure model integrity and governance, including RBAC/ABAC for models, feature stores, data sets, registries, and evaluation artifacts.
Build/enable technical mechanisms for provenance, attestation, signing, and approval workflows (where applicable) across datasets, models, prompts, and deployments.
Drive implementation of runtime protections for AI services (abuse prevention, rate limiting, input/output validation, prompt-injection mitigations, model endpoint hardening, and monitoring).
Threat Modeling, Assurance, and Risk Reduction
Conduct and lead AI/ML-specific threat modeling (data poisoning, model evasion, extraction, inversion, supply-chain, prompt attacks), translate findings into actionable backlogs, and drive remediation.
Define and run security design reviews for AI initiatives; provide clear, pragmatic architecture guidance and document exceptions with risk acceptance paths.
Establish AI security testing approaches (adversarial testing, red-teaming enablement, evaluation security, misuse/abuse cases) and integrate into delivery pipelines.
Tooling, Automation, and Operational Enablement
Design and deliver AI security tooling to improve and automate cybersecurity posture (e.g., controls coverage, policy-as-code, detection engineering, vulnerability management integration, incident response playbooks for AI-specific events).
Define logging/monitoring standards and detection use-cases for AI platforms and LLM apps (drift signals, anomalous access, suspicious prompt patterns, exfiltration indicators, policy violations).
Technical Mentorship & Influence (No Line Management)
Act as the team’s technical mentor: coach engineers through designs, implementations, and trade-offs; raise engineering quality via reviews, pairing, and knowledge sharing.
Lead by influence across Data Science, Engineering, Product, Platform, and Cybersecurity—driving alignment without formal authority.
Create internal enablement materials: runbooks, architecture standards, reusable patterns, and reference implementations.

Ideal Qualifications

Experience: 7+ years in cybersecurity architecture with proven experience securing large-scale LLM deployments and multi-agent workflows.
Technical Proficiency: 5+ years of hands-on capability with agent frameworks (e.g., LangChain, LangGraph, AutoGen) and MLOps platforms.
Framework Knowledge: 3 to 5 years of Deep familiarity with model risk management principles and AI security standards
Common Expectation from all the roles:
Compliance with Client’s responsible AI principles and Acceptable Use policy
Adherence to data residency, privacy (GDPR, HIPAA where applicable), and 21 CFR Part 11 controls where in scope
Third-party risk assessment and SOC 2 Type II (or equivalent) certification
Disclosure of subcontractors and offshore delivery locations
Disclosure of model providers, training data practices, and any use of client data for model improvement (opt-out required)

Dice Id: 10330808
Position Id: 99729-5195-1785956156
Posted 10 hours ago

Company Info

About VDart, Inc.

Fitment

Dice Job Match Score™

🔗 Matching skills to job...

Create job alert

Never miss an opportunity! Create an alert based on the job you applied for.

Similar Jobs

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Security & Agentic AI DevSecOps Engineer

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