Senior AI Agent Engineer

Responsibilities

• Design, build, deploy, and operate agent systems across execution, memory, identity, tool integration, and observability within your area of responsibility
• Work directly with customers and stakeholders to turn needs into functional requirements and measurable acceptance criteria. Personally build working prototypes and production-grade slices of the solution, from design through implementation, testing, and deployment
• Develop agent workflows and production deployments using cloud managed services for AI hosting (e.g., Bedrock, Azure AI Foundry, Vertex AI), selecting services and development tools against the system's requirements
• Implement and operate an LLM gateway & routing layer covering model tiering, quota/cost control, and observability (e.g., LiteLLM, APIM AI Gateway)
• Design memory and context-management approaches for multi-step workflows, including state persistence, retrieval quality, retention, and isolation between users or engagements
• Build and maintain MCP servers and tool integrations for authorized security workflows, including reconnaissance, scanners, controlled exploit tooling, and internal services. Define contracts, permissions, approval boundaries, and recovery behavior
• Establish automated tests and agent evaluations for your components. Distinguish model-quality issues from software defects, and cover tool failures, interrupted execution, and unintended repeated actions
• Diagnose production issues using logs, metrics, and traces. Improve reliability, latency, and cost, and verify that mitigation and recovery work as intended
• Contribute to CI/CD, release checks, rollback plans, code review, and technical documentation. Mentor less experienced engineers and communicate decisions clearly to technical and non-technical stakeholders
• Design and deliver AI agent systems that remain reliable beyond a successful demonstration. In this role, you will own technical decisions and delivery for a defined system or substantial component, from requirements and design through production operation. You will combine strong software engineering with practical…

Languages

Required

• Substantial production software engineering experience, typically five or more years, including at least one year delivering LLM-based agents to production. You can explain your contribution, the trade-offs you made, and the operational results
• Practical experience with cloud managed services for AI hosting (e.g., Bedrock, Azure AI Foundry, Vertex AI), including runtime, state, identity, integration, and observability concerns, and the ability to adopt an unfamiliar platform through working implementations
• Production engineering skills in API design, asynchronous processing, persistence, authentication and authorization, observability, retries, and security boundaries. You can investigate issues that span multiple components
• Experience with automated testing, CI/CD, code review, and AI-assisted development, together with a disciplined approach to validating generated output. You can clarify requirements, communicate designs, and support the growth of other engineers

Nice to have

• Proficiency in English at a B2+ level
• Experience in application security, web penetration testing, or red-teaming within an authorized scope

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