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

As an AI Solutions Architect, you will serve as a trusted technical advisor to enterprise customers, helping them design and implement secure, scalable AI solutions using Quilr. You will work closely with customer architects, engineering leaders, platform teams, and security stakeholders to understand their environments, translate business and security requirements into practical architectures, and guide technical evaluations and deployments. Internally, you will partner with Solutions Engineering, Field Development, Sales, Product, and Core Engineering to ensure that proposed solutions are technically sound, secure, and achievable.

This is a customer-facing, hands-on architecture role for someone who can connect enterprise AI strategy with implementation details and explain technical trade-offs clearly to both technical and business stakeholders.

What You Will Do

Customer Discovery and Solution Design

·    Lead technical discovery sessions with customer engineering, cloud, platform, and security teams to understand their AI use cases, infrastructure, constraints, and risk requirements.

·    Design end-to-end solutions that integrate Quilr with enterprise LLM applications, AI agents, orchestration frameworks, data sources, APIs, and MCP-based workflows.

·    Translate customer needs into solution architectures, integration patterns, deployment plans, and technical success criteria.

·    Create architecture diagrams, technical proposals, implementation roadmaps, and solution documentation tailored to each customer environment.

·    Build technical champions, meet face to face with prospects and develop trusted relationships with our prospects and customers.

Enterprise AI Security Architecture

·    Assess AI application architectures and identify security risks involving prompt injection, sensitive-data exposure, unsafe tool execution, excessive agent permissions, and untrusted context.

·    Design appropriate trust boundaries, authentication and authorization patterns, policy enforcement points, secrets-management practices, and audit logging.

·    Recommend secure deployment patterns across cloud, private cloud, on-premises, and hybrid environments, based on customer requirements and product capabilities.

·    Consider reliability, scalability, performance, observability, maintainability, and operational readiness as part of each proposed solution.

Technical Evaluations and Deployment Guidance

·    Support presales activities, technical workshops, architecture reviews, proof-of-concept (POC) planning, and customer technical evaluations.

·    Define measurable POC success criteria and help customers validate functionality, security controls, integration feasibility, and operational fit.

·    Guide AI Field Developers and Solutions Engineers through implementation decisions, integration dependencies, and complex technical escalations.

·    Collaborate with customer teams to resolve architectural blockers and support a smooth transition from evaluation to production deployment.

Cross-Functional Product Collaboration

·    Partner with Sales, Product, and Engineering to align customer requirements with Quilr's capabilities and roadmap.

·    Capture recurring customer needs, architectural gaps, and field insights, and translate them into actionable product feedback.

·    Develop reusable reference architectures, implementation patterns, and best practices that improve consistency across customer deployments.

·    Communicate technical trade-offs, limitations, risks, and dependencies clearly without overpromising product capabilities.

Required Qualifications

·    Typically 5+ years of experience in solutions architecture, software engineering, cloud architecture, cybersecurity, platform engineering, or a related technical field.

·    Demonstrated experience designing and integrating enterprise applications, APIs, cloud services, or distributed systems.

·    Working knowledge of LLM-powered applications, AI agents, retrieval-augmented generation (RAG), and modern AI application architectures.

·    Strong understanding of cloud infrastructure, networking, identity and access management, authentication, authorization, and enterprise integration patterns.

·    Ability to design secure, scalable, reliable solutions and explain technical trade-offs to engineering teams and business stakeholders.

·    Experience in customer-facing technical consulting, architecture workshops, presales engineering, or technical solution delivery.

·    Strong written and verbal communication skills, including the ability to create architecture diagrams, technical proposals, and implementation plans.

·    Ability to operate effectively in a fast-paced startup environment with evolving requirements and product capabilities.

Nice to Have

·    Experience in AI security, application security, cloud security, or product security.

·    Familiarity with OWASP guidance for LLM applications and agentic AI, threat modeling, AI red-teaming, or penetration-testing methodologies.

·    Knowledge of MCP clients, servers, tools, permissions, and trust boundaries.

·    Experience with AWS, Azure, or GCP, plus Docker, Kubernetes, API gateways, or service meshes.

·    Familiarity with SIEM platforms, secrets management, enterprise identity providers, and security monitoring.

·    Experience defining security controls, AI risk-management practices, or governance requirements for enterprise AI deployments.

·    Relevant cloud, security, or architecture certifications.

What Success Looks Like

·    Deliver clear, secure, and implementable solution architectures that meet customer requirements.

·    Help customers validate technical fit and move efficiently from discovery and evaluation to deployment.

·    Build credibility and trust with customer engineering, architecture, and security stakeholders.

·    Reduce repeated implementation effort through reusable reference architectures and integration patterns.

·    Provide actionable field feedback that informs Quilr's product capabilities and roadmap.

Important Logistical Note

Candidates must possess active, valid US work authorization. Quilr is currently unable to provide visa sponsorship.