Principal Simulation & Reliability Architect

Role Overview

The Principal Simulation & Reliability Architect will lead the design of modular simulation environments, reliability tooling, and observability patterns that help teams understand and improve multi-step agentic AI workflows. This role is both architectural and hands-on: you will prototype internal tools, establish foundational patterns, and collaborate closely with the founder, data scientist, and synthetic data teams.

Responsibilities

• Design modular simulation environments for multi-step agent workflows and decision policies.
• Model interactions among agents, tools, and document flows to surface behavior and failure modes.
• Define evaluation patterns for agentic systems (task success, factuality, procedure adherence, suitability).
• Build regression, validation, and inspection tooling for simulation outputs.
• Identify and instrument key events and metrics for monitoring, triage, and investigation workflows.
• Integrate simulations with modern observability tooling (OpenTelemetry, Arize, Grafana).
• Develop trace schemas and system health signals to support reliability insights.
• Establish architectural patterns and internal frameworks for future engineering hires.
• Contribute to the roadmap and technical foundations of Reins AI’s simulation and reliability platform.

Qualifications

• 6+ years architecting or building complex ML, simulation, workflow, or observability systems.
• Strong Python engineering fundamentals and experience developing internal tooling or frameworks.
• Ability to design abstractions and end-to-end technical architectures.
• Familiarity with multi-step AI workflows or agentic patterns (any framework).
• Strong debugging intuition and systems-thinking mindset.
• Excellent communication skills and comfort working in a fast-moving, founder-led environment.

Preferred Skills

• Experience with simulation frameworks, synthetic data workflows, or agentic evaluation.
• Background in reliability engineering, monitoring, or triage system design.
• Exposure to regulated domains (audit, finance, healthcare).
• Knowledge of distributed systems or ML pipeline design.
• Experience with observability tooling (OpenTelemetry, Arize, Grafana, Datadog).
• Familiarity with agentic frameworks such as LangGraph, Semantic Kernel, or CrewAI.

Employment Details