Head of Infrastructure and AI Systems

Radix•Scottsdale, AZ

About The Position

Radix is revolutionizing how the multifamily world makes decisions. From investment to divestment, and everything in between, Radix brings data transparency, market intelligence, and acquisition modeling into a seamless ecosystem that turns the industry's best insights into confident decisions. You will be a foundational leader in building an agentic first engineering organization where AI agents operate as first class contributors alongside human engineers. This role sits at the center of infrastructure, reliability, developer experience, and AI orchestration, with full ownership of how these systems interconnect and evolve. You will play a defining role in architecting and operationalizing the Agent Harness, shaping how agents safely, effectively, and reliably operate across our platform. This is a rare opportunity to influence not just systems, but the future operating model of engineering at a company with real traction, meaningful complexity, and high stakes.

Requirements

Brings 10 or more years of experience in platform, infrastructure, or systems engineering, with deep exposure to operating complex production systems rather than only building them.
Demonstrates deep expertise in AWS core services, along with hands on experience in Azure, including identity federation and cross cloud integration patterns.
Possesses production level experience with Kubernetes and strong proficiency in Databricks, including cluster management, scaling, security, Spark tuning, and data governance.
Treats infrastructure as code as a default practice, with Terraform preferred, and builds robust observability systems across metrics, tracing, and logging with a proactive mindset.
Thinks in systems, maintaining a full stack perspective from DNS through application layers to agent behavior, while identifying risks and anti patterns early.
Operates with a strong Day 2 mindset, embedding reliability, operational readiness, and security as core design principles from the outset.
Has real world experience integrating AI and LLM systems into production environments, with a deep understanding of agent safety, trust boundaries, observability, and failure modes.
Is motivated by the challenge of enabling reliable collaboration between human engineers and AI agents, while staying current on the evolving AI infrastructure landscape.
Exhibits curiosity, resilience, and adaptability when navigating ambiguity and working within fast moving, evolving systems.
Thrives in high growth startup environments with strong ownership, operating with autonomy and transparency while leading through hands on execution.
Excels in small teams where impact is immediate, bringing a builder mindset that proactively addresses gaps and strengthens systems.

Responsibilities

Architect and build the Agent Harness, the system of controls, context, and guardrails that govern how AI agents are deployed, monitored, constrained, and evolved across the platform.
Define and enforce operational contracts between human engineers and AI agents, including access boundaries, audit trails, rollback mechanisms, and trust boundaries.
Translate desired agent outcomes into concrete infrastructure requirements, including defining what context agents require, what systems they can interact with, and how failures are handled.
Own the reliability and observability of agent driven workflows end to end, from initial invocation through downstream system impact.
Evaluate, select, and integrate LLM tooling, agent orchestration frameworks, and emerging AI native infrastructure primitives as the ecosystem evolves.
Own and operate all cloud infrastructure across AWS as primary and Azure, including compute, networking, storage, security posture, and cross cloud integration patterns.
Architect, operate, and continuously improve Kubernetes clusters at production scale, including workload isolation, autoscaling strategies, cost optimization, and multi environment lifecycle management.
Own and evolve the Databricks environment, including cluster configuration, job orchestration, access controls, and data governance, while defining clear infrastructure contracts between data engineering and the broader platform.
Drive infrastructure as code practices with Terraform preferred and with a strong emphasis on repeatability, auditability, and operational simplicity.
Ensure infrastructure is appropriately sized and cost efficient, intentionally architected for current team needs rather than speculative future scale.
Own continuous integration and continuous delivery pipelines that enable fast, safe, and confident software delivery, with explicit consideration for how AI agents participate in the delivery lifecycle.
Identify and eliminate friction across the developer experience, including local development, testing, deployment, and observability workflows.
Establish and enforce engineering standards that proactively prevent anti patterns before they manifest as production incidents.
Apply site reliability engineering principles including service level objectives, error budgets, blameless post mortems, and continuous reliability improvements.
Lead incident response end to end, owning detection, triage, resolution, and preventative follow through.
Build and maintain runbooks, operational playbooks, and automation that make on call responsibilities sustainable for a small, high performing team.