Chief Architect

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.
• Create 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-term platform leverage.
• Product, Business, and Customer Impact
• Connect technical decisions directly to customer value, product velocity, implementation speed, scalability, reliability, and long-term platform advantage.
• Partner with Product and Engineering leadership to translate ambitious product goals into executable architecture and technical roadmaps.
• Understand enterprise customer complexity and design systems that support real-world operational variability without creating unsustainable technical debt.
• Support strategic customer, partner, and vendor architecture discussions where deep technical credibility is required.
• Help evaluate technical implications of major product, partner, platform, and commercial decisions.
• Engineering Culture and Technical Talent
• Mentor and elevate Staff Engineers, Principal Engineers, architects, and senior engineering leaders across the organization.
• Raise the bar for technical judgment, system design, architecture reviews, documentation, code quality, operational discipline, and engineering craftsmanship.
• Help assess, attract, and develop senior technical talent, including Staff, Principal, and architect-level engineers.
• Build a culture of disciplined technical thinking, direct debate, clear decision-making, and pragmatic execution.
• Ensure architectural decisions are documented clearly, understood broadly, and translated into executable engineering plans.
• Security, Compliance, and Enterprise Readiness
• Own the architectural approach to enterprise security, data governance, privacy, compliance, auditability, and resilience.
• Ensure architecture supports SOC 2, ISO 27001, data residency, access control, tenant isolation, regulatory requirements, and other needs of large global enterprise customers.
• Partner with Security, Infrastructure, Product, and Engineering teams to make security and compliance foundational architectural properties rather than after-the-fact controls.

Required Skills and Qualifications

• Experience Level
• 15+ years of experience building and scaling large-scale software systems.
• Prior experience as a Chief Architect, Distinguished Engineer, Principal Architect, very senior Principal Engineer, or equivalent senior technical leadership role.
• Demonstrated success designing and scaling mission-critical, multi-tenant SaaS platforms for enterprise customers.
• Deep hands-on experience with large-scale distributed systems, cloud-native architecture, data platforms, APIs, event-driven systems, platform engineering, and high-availability production environments.
• Proven ability to operate as a hands-on technical leader who can credibly engage in code-level and design-level discussions with senior engineers.
• Experience making major architecture decisions for systems with high scale, high reliability requirements, complex domain logic, and long product lifecycles.
• Experience modernizing large production systems incrementally while maintaining uptime, backward compatibility, customer trust, and engineering velocity.
• Experience building or supporting AI, ML, optimization, decisioning, forecasting, or data-intensive products in production.
• Strong understanding of observability, incident response, performance engineering, reliability architecture, capacity planning, and operational excellence.
• Ability to communicate complex technical concepts clearly to engineering teams, executives, customers, and cross-functional partners.
• Technical Depth
• Deep expertise in distributed systems, system design, cloud architecture, service-oriented or microservices architectures, APIs, event-driven systems, and large-scale data processing.
• Strong experience with cloud platforms such as AWS, GCP, or Azure.
• Strong experience with modern engineering practices, including architecture reviews, design documents, code reviews, testing strategy, deployment architecture, observability, and production operations.
• Experience with languages such as Java, Kotlin, Go, Python, Scala, or similar modern backend languages.
• Ability to reason deeply about tradeoffs involving performance, latency, throughput, cost, reliability, developer productivity, extensibility, and maintainability.
• Leadership and Judgment
• Strong architectural judgment: knows when to simplify, when to re-platform, when to tolerate debt, and when to force a hard technical reset.
• Demonstrated ability to make bold technical decisions with incomplete information and drive alignment across senior technical and executive stakeholders.
• Ability to influence without relying on hierarchy, while still being willing to make clear decisions when consensus is insufficient.
• Strong product and business judgment; able to connect architecture decisions to customer outcomes, implementation speed, product differentiation, and company strategy.
• Pragmatic builder mindset: balances long-term architecture with startup speed, customer urgency, and execution realities.

Preferred Qualifications and Attributes

• Experience building platforms used by millions of users or supporting very large-scale enterprise operations.
• Experience at companies known for large-scale distributed systems, AI, cloud infrastructure, enterprise SaaS, workforce management, supply chain, fintech, commerce, logistics, or other complex operational domains.
• Familiarity with optimization engines, scheduling algorithms, forecasting systems, constraint solvers, real-time decision systems, or mathematically complex production systems.
• Experience architecting AI-native product capabilities, including LLM-enabled workflows, AI agents or copilots, model evaluation, retrieval systems, feedback loops, and production monitoring.
• Experience building internal developer platforms, platform infrastructure, data platforms, ML pipelines, or large-scale analytics systems.
• Experience designing highly configurable enterprise platforms, ensuring configurability without allowing uncontrolled customization or product fragmentation.