Description
We are hiring a Senior AI Engineer to build and operate production AI systems and infrastructure. In this role, you will work on AI agents, MCP servers, agent evaluation, and Temporal-based workflow frameworks. This is a hands-on engineering role for someone who can move between backend systems, AI application development, deployment, and production support. You will help build reliable services and frameworks that make AI agents usable, measurable, and dependable in production.
Responsibilities
•Build and deploy AI agents that use tools, call services, manage context, and execute multi-step tasks.
•Develop and maintain MCP servers and tool integrations used by internal AI systems.
•Build workflow infrastructure using Temporal or similar orchestration frameworks.
•Design and implement agent evaluation flows, test suites, quality metrics, and feedback loops.
•Design backend services, APIs, persistence layers, and platform components for AI applications.
•Work with SQL and NoSQL databases, queues, distributed systems, and cloud-native infrastructure.
•Build CI/CD pipelines, Docker images, Kubernetes deployments, monitoring, and operational tooling.
•Debug production issues across services, workflows, agents, tools, models, and infrastructure.
•Collaborate with engineering, product, infrastructure, and data science teams to turn requirements into reliable systems.
What we need to see
•5+ years of experience and a practical understanding of how AI agents are built, including tool use, state, context, and multi-step execution.
•Bachelor's degree or equivalent experience.
•Experience with at least one open-source agent or LLM application framework, such as LangChain/LangGraph.
•Strong software engineering background, especially in backend development.
•Experience with distributed systems, workflow orchestration, and production service design.
•Hands-on experience with SQL and NoSQL databases.
•Experience with Docker, Kubernetes, CI/CD pipelines, and production deployment workflows.
Requirements
•Experience working with both traditional machine learning systems and contemporary LLM or agentic applications.
•Excellent judgment about when agent-based approaches are appropriate—and when a simpler solution is more effective.
•Experience making AI behavior measurable, observable, explainable, and safe in production.
•Experience optimizing inference systems for performance, infrastructure efficiency, and operating cost.
•A history of guiding engineers or heading cross-departmental technical projects.
We are looking for engineers who care deeply about technical quality. They take ownership from building through production. They are motivated by the challenge of turning advanced AI capabilities into reliable products.
