Senior Engineer, AI/ML

We operate a multi-tenant automotive SaaS platform serving thousands of dealer groups across the United States, running on a native AWS (NAWS) backend: Lambda, EventBridge, DynamoDB, S3, Step Functions. That platform works. Now we are making it think: production agentic AI systems built code-first and deployed on AWS Bedrock AgentCore, connected to real systems, making decisions and executing workflows against real dealer data and real money.

This is a hands-on builder role. Expect the large majority of your time in code. But we are not hiring a pair of hands. Every agent you ship makes decisions that touch dealer revenue, so we need an engineer who understands the business problem before writing the technical solution: someone who asks what a workflow is worth, weighs build cost against dealer impact, and knows when the right answer is a simpler tool, or no agent at all.

What you will own

Building production agents in Strands/LangGraph on AgentCore: agent logic, tool orchestration, and multi-agent workflows deployed on AgentCore Runtime with Memory, Identity, and Gateway.

Tool interfaces: framework-agnostic Python tools, MCP servers, and AgentCore Gateway targets that let agents safely call production APIs.

Evaluation harnesses: offline/online eval pipelines and LLM-as-judge CI/CD gates that catch regressions before dealers do.

Guardrails and safe failure: approval gates and rollback paths that determine whether an agent fails safe or fails loud.

Cost and latency management: per-agent cost tracking and multi-model routing so each workflow earns more than it costs.

Production observability: AgentCore session tracing, OpenTelemetry, and CloudWatch dashboards for every agent you ship.

Tech environment: Strands Agents SDK on AWS Bedrock AgentCore (Runtime, Gateway, Memory, Identity, Observability); MCP servers; Bedrock Guardrails; Anthropic models (Haiku, Sonnet, Opus); NAWS stack (Lambda, EventBridge, DynamoDB, S3, Step Functions,