Lead AI Agent Engineer

Responsibilities

• Lead solution design across agent execution, memory, identity, tool integration, and observability Turn customer needs into clear requirements, compare alternatives, and explain the business and technical reasons for the chosen approach
• Guide the team's use of cloud managed services for AI hosting (e.g., Bedrock, Azure AI Foundry, Vertex AI) Establish implementation patterns and validate platform choices against reliability, security, cost, and operational requirements
• Design and guide implementation of an LLM gateway & routing layer covering model tiering, quota/cost control, and observability (e.g., LiteLLM, APIM AI Gateway)
• Design and review memory, context, and state-management approaches for long-running workflows, including recovery, data retention, access boundaries, and isolation between users or engagements
• Set integration standards for MCP servers and tools used in authorized security workflows, including reconnaissance, scanners, controlled exploit tooling, and internal services Define permissions, approval requirements, auditability, and containment of failures
• Establish a balanced testing and evaluation strategy covering software correctness, agent behavior, security boundaries, and operational performance Define release criteria and ensure the team can demonstrate that they are met
• Guide production readiness and incident response, including observability, cost controls, staged delivery, rollback, and recovery Use operational evidence to prioritize improvements and technical debt
• Plan and prioritize technical delivery with product, security, and engineering stakeholders Break work into achievable milestones, delegate ownership, manage dependencies, and resolve technical and delivery impediments
• Personally build working prototypes and production-grade slices of the solution, taking them through implementation, testing, and deployment
• Lead complex troubleshooting and develop other engineers through design reviews, coaching, and constructive feedback

Languages

Required

• Deep 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 evidence of leading a team through adoption of an unfamiliar platform
• Practical expertise in MCP, tool orchestration, context and memory management, and agent evaluation, supported by a strong understanding of distributed-system failures and security boundaries
• Experience establishing or improving testing, CI/CD, observability, and engineering practices You can explain how these practices improved quality, delivery, or operational outcomes

Nice to have

• Substantial experience delivering and operating production software, typically five or more years, including at least one year shipping LLM-based agents to production You also bring a demonstrated record of technical leadership and team delivery
• English proficiency at B2 level or higher
• Experience leading engineering work in application security, penetration testing, red-teaming, or security automation within an authorized scope

Other open roles here

• Senior/Lead Power BI Software EngineerRemote · Ukraine
• Delivery Manager (AI Native)Remote · Ukraine
• Senior Financial Manager (m/f/d)Remote · Germany
• Principal, Business ConsultingRemote · United States
• Senior Delivery Manager - .NET/JavaIndia · On-site · relocation