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.