GovUS
This role focuses on developing the secure, scalable back-end infrastructure that powers AI agent workflows and integrations for local government workflows. You will interact with APIs and services to orchestrate agent activities, enabling seamless interaction with external systems across both cloud and on-premise platforms. Responsibilities include building authentication and data exchange pipelines, ensuring compliance with enterprise security standards, and supporting deployment and observability in collaboration with DevOps.
Responsibilities:
Build infrastructure to allow AI agents to connect to and interact with external systems, APIs, and data sourcesacross both cloud (e.g., AWS, Azure, GCP) and on-premise environments. Design and implement robust, scalable, and secure back-end services to power AI agent workflows and orchestration. Develop integration pipelines for authentication, data exchange, and command execution between agents and third-party enterprise tools (e.g., ERPs, databases, legacy systems). Collaborate with AI and ML teams to expose model interfaces and action frameworks via infrastructure. Implement secure communication protocols, handle sensitive credentials, and ensure compliance with enterprise security standards. Support deployment, monitoring, and observability for infrastructure services. Create documentation and tooling that make deployment and integration repeatable and modular. Participate in code reviews, architecture discussions, and design decisions with a cross-functional engineering team. Qualifications:
Experience in back-end development using languages such as Python, Go, Java, or Node.js. Experience designing and maintaining distributed systems, APIs, and event-driven architectures. Proficiency with containerization (Docker, Kubernetes) and microservices deployment. Deep understanding of cloud services (AWS, GCP, or Azure) and networking fundamentals. Strong experience integrating with enterprise authentication systems (OAuth2, SAML, LDAP). Experience building or supporting infrastructure for AI/ML systems, agent platforms, or workflow orchestration tools.
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Build infrastructure to allow AI agents to connect to and interact with external systems, APIs, and data sourcesacross both cloud (e.g., AWS, Azure, GCP) and on-premise environments. Design and implement robust, scalable, and secure back-end services to power AI agent workflows and orchestration. Develop integration pipelines for authentication, data exchange, and command execution between agents and third-party enterprise tools (e.g., ERPs, databases, legacy systems). Collaborate with AI and ML teams to expose model interfaces and action frameworks via infrastructure. Implement secure communication protocols, handle sensitive credentials, and ensure compliance with enterprise security standards. Support deployment, monitoring, and observability for infrastructure services. Create documentation and tooling that make deployment and integration repeatable and modular. Participate in code reviews, architecture discussions, and design decisions with a cross-functional engineering team. Qualifications:
Experience in back-end development using languages such as Python, Go, Java, or Node.js. Experience designing and maintaining distributed systems, APIs, and event-driven architectures. Proficiency with containerization (Docker, Kubernetes) and microservices deployment. Deep understanding of cloud services (AWS, GCP, or Azure) and networking fundamentals. Strong experience integrating with enterprise authentication systems (OAuth2, SAML, LDAP). Experience building or supporting infrastructure for AI/ML systems, agent platforms, or workflow orchestration tools.
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