About QuilrAI

QuilrAI is an agent-native security company founded by security veterans to secure and operationalize the agentic enterprise across the entire agent lifecycle. We treat AI as a deployment problem—ensuring agents are assessed, governed by policy, and deployed via approved blueprints before they reach production, with every agent action decided before execution and verified afterward.

Headquartered in Austin, Texas, we partner with high-growth tech firms and enterprise customers across financial services, legal, and business operations to protect critical workflows against prompt injection, data exfiltration, and non-deterministic model behavior.

The Role

The Forward Deployment Engineer (FDE) works directly inside customer environments to deploy, adapt, and operationalize AI and Data security solutions. This role is deeply hands-on and customer-facing, focused on making AI, DLP, and Identity controls work under real-world constraints—often ahead of full product maturity. In addition to technical execution, the FDE plays a critical role in explaining architectural decisions, DLP limitations, and trade-offs to customer technical teams. You will combine hands-on deployment with clear technical storytelling and collaborative problem solving, helping customers understand not only what is being deployed, but why it works and where its boundaries are.

What You Will Do

• Deployment & Integration

• Deploy AI security solutions across cloud, hybrid, and on-premises customer environments.

• Integrate AI agents and workflows using LangChain, LangGraph, and agent orchestration patterns involving human and non-human identities.

• Integrate identity and access management solutions, including Microsoft Entra ID (Azure AD), Okta, OAuth, token enforcement, and service identities.

• Implement data protection and enforcement workflows, including Data Loss Prevention (DLP) inspection, policy enforcement, and audit logging.

• Hands-On Engineering & Troubleshooting

• Develop custom scripts, connectors, and workflows using Python and Docker to support AI security deployments.

• Troubleshoot and resolve technical issues across AI pipelines, agent workflows, identity flows, authorization boundaries, data movement, and policy enforcement points.

• Design and implement practical workarounds to address missing features, product limitations, and customer-specific technical challenges.

• Maintain deployment artifacts, scripts, and configuration files in GitHub to ensure version control and consistency across deployments.

• Customer Workshops & Collaborative Problem Solving

• Participate in and co-lead technical workshops, architecture reviews, and whiteboarding sessions during customer deployments.

• Collaborate with customer engineers and architects to refine AI access paths, define identity trust boundaries, and validate data protection and enforcement strategies.

• Identify environmental constraints, integration dependencies, and deployment challenges early to drive technical alignment.

• Technical Communication, DLP & Customer Alignment

• Explain AI security architectures and deployment designs, demonstrating how AI workflows, identity controls, and data protection mechanisms work together.

• Communicate where security enforcement occurs, identify enforcement limitations, and highlight potential coverage gaps.

• Lead technical discussions on DLP detection and enforcement capabilities, including the structural limitations of traditional DLP in AI-driven and agent-based workflows.

• Address common misconceptions about DLP coverage and help customers understand risk trade-offs, enforcement gaps, and compensating controls without overstating security capabilities.

• Feedback & Continuous Improvement

• Document deployment learnings, edge cases, technical challenges, and failure modes encountered during customer implementations.

• Provide structured feedback to Product and Engineering teams to influence the product roadmap, improve capabilities, and strengthen product reliability.

• Standardize successful deployment patterns, reusable configurations, and troubleshooting procedures to improve deployment efficiency across customer environments.

What We Are Looking For

Required Qualification

• Hands-on experience with AI/LLM systems, AI security architectures, and data protection mechanisms.

• Strong understanding of Data Loss Prevention (DLP), including data classification, content inspection, policy enforcement, real-world limitations, blind spots, and potential bypass scenarios.

• Solid knowledge of Identity and Access Management (IAM), including Single Sign-On (SSO), OAuth 2.0, OpenID Connect (OIDC), token management, service identities, and policy engines.

• Proficiency in Python, Docker, and cloud-native technologies across AWS, Microsoft Azure, or Google Cloud Platform (GCP), with familiarity in endpoint security and device trust integrations such as Microsoft Intune and Jamf Pro.

• Strong debugging, troubleshooting, problem-solving, and systems-thinking skills, with the ability to communicate complex technical concepts clearly and work directly with customers in high-stakes technical environments.

Nice to have

• Familiarity with AI agent frameworks and orchestration tools such as LangChain and LangGraph, including security considerations for agentic workflows.

• Experience integrating identity and access management solutions such as Microsoft Entra ID, Okta, OAuth/OIDC, and service identities.

• Knowledge of AI security risks, DLP enforcement mechanisms, data leakage prevention, prompt injection, and agent permission boundaries.

• Experience with cloud security, containerization, CI/CD pipelines, GitHub, and infrastructure-as-code tools across AWS, Azure, or GCP.

• Experience conducting customer workshops, technical demonstrations, proof-of-concept deployments, and documenting reusable integration patterns and deployment best practices.