Senior/Lead AI/ML Engineer
Ryz Labs
| Company | Ryz Labs |
| Category | Engineering |
| Location | US |
| Remote | Remote |
| Employment | Contract |
| Level | Lead |
| Salary | Not stated by the employer |
| Posted | 16 Jun 2026 |
| Last verified | 12 Aug 2026 |
| Source | The employer's own careers page (company_site) |
Description
At 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 Responsibilities
Design and implement LLM-powered conversational agents in production
Build robust agent orchestration systems, including:
State management
Pause/resume flows
Journey and mode routing
Failure handling and recovery
Develop and integrate tooling via MCP or gateway-style architectures, including:
Schema design
Authentication & authorization
Idempotency handling
Auditability
Create and maintain custom evaluation frameworks for LLM performance and reliability
Operate and deploy systems in a production AWS environment
Collaborate closely with internal stakeholders in an embedded delivery model
Required Qualifications
Proven experience shipping production LLM agents with tool use
Strong expertise in Python
Hands-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:
EKS
Lambda / EventBridge
IAM
Bedrock / Agent runtimes
Logging and monitoring systems
Experience with at least one LLM observability/evaluation platform in production (e.g., Arize AX, Langfuse, Phoenix)
Preferred / Nice to Have
Experience with AWS Bedrock and AgentCore (Runtime + Gateway)
Familiarity with Strands, or ability to ramp quickly from LangGraph / LangChain
Experience implementing governed memory systems
Background in regulated environments (fintech, banking, etc.)