Headquarters: Remote, United States

Chief Architect

Remote - US

Legion Technologies is building the AI-powered operating system for the hourly workforce. We are hiring a Chief Architect to serve as Legion’s most senior hands-on technical leader and to shape the technical foundation that will power the next decade of product innovation.

This is not a strategy-only or advisory role. The Chief Architect must live close to the code, go deep with engineering teams, challenge assumptions, write and review technical designs, make high-consequence architecture decisions, and help turn bold product ambitions into scalable, reliable production systems.

This person will own architecture and technical decision-making for the most complex parts of Legion’s Workforce Management platform, including AI, optimization, forecasting, scheduling, distributed systems, data platforms, real-time decisioning, enterprise scalability, security, compliance, and developer productivity.

The ideal candidate has built and scaled mission-critical software used by large enterprise customers, has deep technical credibility with senior engineers, and can operate fluidly between code-level decisions and long-term platform strategy. They are pragmatic, decisive, opinionated, and comfortable making hard tradeoffs when simplicity, scalability, reliability, or customer trust require it.

This role reports to the Head of Product, Engineering, and Support. This is an open role currently being filled on an interim basis by the CEO.

Responsibilities and Duties

Hands-On Architecture and Technical Decision-Making

Serve as Legion’s senior-most hands-on technical authority for complex architecture, system design, and platform evolution.

• Personally dive into code, design documents, production incidents, performance bottlenecks, and implementation tradeoffs to diagnose root causes and guide technical direction.

• Lead the hardest technical decisions across AI systems, optimization engines, distributed systems, data architecture, reliability, scalability, security, and platform modernization.

• Write prototypes, reference implementations, architecture decision records, technical specifications, and design patterns where needed to unblock teams and establish clear direction.

• Make high-consequence technical decisions with incomplete information, balancing correctness, simplicity, speed, scalability, reliability, cost, and long-term maintainability.

• Challenge architectural drift, unnecessary complexity, weak abstractions, and short-term decisions that create long-term platform risk.

• Partner directly with Staff, Principal, and senior engineering leaders in design reviews, code-level discussions, and implementation planning.

Platform Architecture and Technical Vision

• Define and own the long-term architecture for Legion’s AI-driven Workforce Management platform across application services, data infrastructure, ML systems, optimization engines, APIs, integration architecture, developer platforms, and enterprise-scale operations.

• Establish engineering-wide standards for system design, scalability, performance, reliability, extensibility, observability, security, and maintainability.

• Serve as the final architectural authority for major platform initiatives and technical decisions with long-term consequences.

• Identify opportunities to reduce complexity, improve system efficiency, accelerate engineering velocity, and unlock new product capabilities.

• Proactively surface technical debt, architectural risk, and platform constraints before they compound into customer, product, or engineering velocity issues.

• Create pragmatic migration paths from current-state architecture to target-state architecture while maintaining uptime, customer trust, backward compatibility, and delivery speed.

• Ensure Legion’s architecture supports enterprise configurability and extensibility without allowing uncontrolled customization or product fragmentation.

AI, Optimization, and Decision Systems

• Architect AI-native product capabilities across forecasting, scheduling, labor optimization, recommendations, copilots or agents, anomaly detection, decision automation, and other intelligent workforce management use cases.

• Define the architecture for production AI systems, including data pipelines, feature platforms, model training, model serving, retrieval, evaluation, monitoring, feedback loops, and continuous improvement.

• Make clear build-versus-buy decisions across LLMs, classical ML, optimization solvers, retrieval systems, evaluation frameworks, and internal AI platforms.

• Partner closely with Product, Data Science, and Engineering to turn mathematically complex labor optimization problems into reliable, scalable, explainable production systems.

• Establish standards for AI system quality, including accuracy, latency, cost, explainability, drift detection, reliability, customer-specific behavior, and production observability.

• Ensure Legion’s AI capabilities remain differentiated, defensible, enterprise-ready, and deeply integrated into operational workflows rather than bolted on as superficial features.

Distributed Systems, Data, and Enterprise Scale

• Drive architecture for large-scale, multi-tenant SaaS systems serving complex enterprise customers with high availability, performance, security, and compliance expectations.

• Lead technical decisions involving microservices, APIs, event-driven architecture, distributed data processing, real-time systems, data governance, and large-scale analytics.

• Improve the architecture for observability, incident analysis, performance engineering, capacity planning, reliability, and operational excellence.

• Use production data, incidents, escalations, and customer operational patterns as inputs into architecture and platform improvement.

• Ensure architectural decisions support global scale, data residency, enterprise integrations, configurability, extensibility, and long-