You have opted out of job matches in .
To undo this, go to the 'Skills and Interests' section of your preferences.
Shared by 
12 days ago
Requirements and responsibilities
About the roleAt Ryz Labs we are looking for a Senior / Lead AI/ML Engineer to design and ship production-grade conversational AI agents with advanced tool use and orchestration capabilities. This is a hands-on role where you’ll work closely with the internal team to build, scale, and harden real-world LLM systems in a production AWS environment. You’ll be embedded with the team, contributing directly to architecture decisions and implementation.Key ResponsibilitiesDesign and implement LLM-powered conversational agents in productionBuild robust agent orchestration systems, including:State managementPause/resume flowsJourney and mode routingFailure handling and recoveryDevelop and integrate tooling via MCP or gateway-style architectures, including:Schema designAuthentication & authorizationIdempotency handlingAuditabilityCreate and maintain custom evaluation frameworks for LLM performance and reliabilityOperate and deploy systems in a production AWS environmentCollaborate closely with internal stakeholders in an embedded delivery modelRequired QualificationsProven experience shipping production LLM agents with tool useStrong expertise in PythonHands-on experience with:Agent orchestration patterns (state machines, routing, retries, etc.)Tool integration frameworks (MCP or similar gateway approaches)Experience working in production AWS environments, including:EKSLambda / EventBridgeIAMBedrock / Agent runtimesLogging and monitoring systemsExperience with at least one LLM observability/evaluation platform in production (e.g., Arize AX, Langfuse, Phoenix)Preferred / Nice to HaveExperience with AWS Bedrock and AgentCore (Runtime + Gateway)Familiarity with Strands, or ability to ramp quickly from LangGraph / LangChainExperience implementing governed memory systemsBackground in regulated environments (fintech, banking, etc.)
Optionally, you can add more information later (benefits, pre-screening questions, etc.)