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 Field Developer, you will work directly with customer engineering, platform, and security teams to implement and customize Quilr's AI security solutions. You will write production-quality code, build integrations with customer AI applications and infrastructure, troubleshoot deployment challenges, and translate real-world customer requirements into working technical solutions. This role is ideal for a practical engineer who enjoys solving ambiguous problems, building quickly, and working closely with both customers and core product engineering teams.

What You Will Do

·    Build Customer Integrations: Develop and maintain Python-based integrations, APIs, SDKs, adapters, and connectors for enterprise AI applications and infrastructure.

·    Integrate Quilr's security capabilities into LLM-powered applications, AI agents, orchestration frameworks, and MCP-based workflows.

·    Build proof-of-concept implementations and turn successful prototypes into reliable, maintainable solutions.

·    Deploy and Customize AI Security Solutions: Implement Quilr within customer environments, including cloud infrastructure, containerized workloads, and enterprise application stacks.

·    Configure integrations with customer identity systems, logging platforms, API gateways, and security tools; support on-premises, private-cloud, and hybrid deployments where applicable.

·    Troubleshoot Real-World Engineering Challenges: Debug API, authentication, networking, configuration, and runtime issues.

·    Investigate AI-specific security challenges such as prompt injection, unsafe tool execution, insecure agent permissions, and untrusted context.

·    Develop test cases and reproducible examples to identify integration gaps and improve reliability.

·    Collaborate with Customers and Product Engineering: Work with customer developers, DevOps teams, and security engineers to understand implementation requirements.

·    Translate field problems into actionable engineering issues, product enhancements, and reusable integration patterns.

·    Improve Engineering Quality: Write unit tests, integration tests, and deployment automation; follow secure coding practices and contribute to reusable code and technical documentation.

Required Qualifications

·    Approximately 1–4 years of experience in software engineering, backend development, AI engineering, DevOps, or a related technical field; strong early-career candidates are encouraged to apply.

·    Strong hands-on programming skills in Python and familiarity with REST APIs, JSON, authentication, and common software engineering practices.

·    Experience building or integrating applications that use LLMs, AI agents, or modern AI development frameworks.

·    Familiarity with Git, Linux, debugging, testing, and basic CI/CD workflows.

·    Understanding of application deployment concepts, including Docker, cloud environments, environment configuration, and application logging.

·    Ability to troubleshoot unfamiliar systems and work independently in ambiguous environments.

·    Strong communication skills and willingness to collaborate directly with enterprise customers.

Nice to Have

·    Experience with LangChain, LangGraph, LlamaIndex, or comparable AI orchestration tools.

·    Familiarity with MCP servers, clients, tools, and integration patterns.

·    Exposure to AWS, Azure, GCP, Kubernetes, or private-cloud deployments.

·    Knowledge of application security, OWASP guidance, threat modeling, or penetration testing.

·    Familiarity with LLM evaluations, guardrails, observability, and AI red-teaming.

·    Experience integrating enterprise identity, secrets management, SIEM, or security monitoring tools.

What Success Looks Like

·    Deliver working customer integrations within agreed timelines.

·    Reduce deployment friction through reusable connectors and automation.

·    Resolve complex customer implementation issues effectively.

·    Improve product quality by converting field feedback into actionable engineering contributions.

·    Enable customers to operate Quilr reliably in their environments.

Important Logistical Note

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