Lead Software Architect

The Lead Software Architect will be responsible for ensuring the design and development of software architecture aligns with business objectives and scalability. You will partner cross-functionally to define system requirements and design solutions that integrate seamlessly into complex engineering systems. You will be responsible for driving innovation through prototyping and evaluating new tools, frameworks, and methodologies to enhance development processes  

Job Description

Software Lead Architect (B4 / 8C)

Position Summary

The Software Lead Architect owns the cloud architecture and technology roadmap for CLSS products and platforms. The core focus is scalable, secure, resilient cloud-native systems, complemented by AI-enabled development and architecture, including integration of generative AI and agents where they add product and engineering value.

As the technical authority across engineering teams, the architect partners with Product Management, Engineering, System Architecture, and Cyber Security to govern platform design, modernization, and delivery. The role balances reliability, security, performance, cost, and enterprise requirements while enabling responsible adoption of AI capabilities.

Key Responsibilities

Architecture & Technical Leadership

• Own the end-to-end architecture of CLSS products and cloud platforms, including shared services, platform boundaries, integration patterns, deployment models, and AI capabilities where appropriate.
• Set architectural direction across engineering teams through reusable standards, frameworks, and clear domain ownership.
• Govern architecture reviews, decision rights, guardrails, exceptions, and conformance; maintain an auditable record of key decisions, risks, and trade-offs.

Solution Design

• Design cloud-native solutions using distributed systems, microservices, API-first and event-driven integration, containers, Kubernetes, and managed or serverless services; define data flows and architecture contracts.
• Design multi-tenant cloud services with tenant isolation, identity and access management, network security, data protection, and regional deployment; extend these controls to AI agents and tool access.
• Define scalability, availability, and performance requirements, including service-level objectives, capacity planning, disaster recovery, backup and restore, and failure testing across cloud services and integrations.
• Optimize cloud consumption through workload sizing, autoscaling, managed-service selection, FinOps, and cost-performance trade-offs, including model and tool usage costs for AI workloads.
• Enable safe platform evolution through API and service versioning, backward compatibility, data migration, and low-downtime deployment, including lifecycle management of integrated models and agents.

Engineering Excellence

• Accelerate cloud delivery with reusable reference architectures, platform services, infrastructure as code, and automation; enable AI-assisted development, testing, and documentation with engineering review and quality controls.
• Embed automated security and quality checks, deployment automation, and production observability into DevSecOps and CI/CD; track reliability, performance, cost, and architecture health, with evaluation gates for AI-enabled features.
• Lead production readiness and incident learning for business-critical cloud services; prioritize technical debt, platform modernization, systemic risks, and cross-team dependencies with engineering leaders.

Stakeholder Collaboration

• Partner with Product Management, Engineering, UX, System Architecture, and Cyber Security to translate business needs into cloud solutions and enterprise integrations, identifying suitable opportunities for AI-enabled workflows.
• Communicate architecture strategy, investment options, and trade-offs to senior leadership, linking platform investments to product outcomes, risk, and total cost of ownership; serve as the technical escalation point for cross-cutting decisions.

Technology Strategy & AI Enablement

• Define the cloud modernization and AI-enablement roadmap for CLSS; evaluate emerging technologies and prototype generative and Agentic AI use cases, including retrieval, orchestration, and tool integration, before production adoption.
• Establish responsible AI adoption guardrails for agent autonomy, human oversight, permissions, data access, prompt and model security, auditability, and regulatory compliance.

Mentorship & Talent Development

• Build cloud architecture and AI-enabled engineering capability through mentoring, design coaching, and reusable guidance; lead a distributed architecture community supporting technical leadership development and succession.

Qualifications

Education

• Bachelor's or Master's degree in Computer Science, Software Engineering, Information Technology, or related discipline.

Experience & Key Skills

• 12+ years of software engineering experience, including 5+ years in architecture, technical leadership, or solution design roles.
• Proven experience as a principal or lead architect delivering business-critical enterprise cloud platforms, with practical understanding of infrastructure as code, DevSecOps, CI/CD, observability, and disaster recovery.
• Deep expertise in public cloud platforms such as Azure or AWS, distributed systems, microservices, containers and Kubernetes, managed or serverless services, API and event-driven integration, data architecture, security, resilience, and cost optimization.
• AI-enabled development and architecture skills, including AI-assisted engineering and hands-on Agentic AI integration: large language models, retrieval-augmented generation, orchestration, tool use, lifecycle management, evaluation, and responsible AI controls within cloud platforms.
• Ability to align engineers and senior stakeholders without relying solely on direct authority, and clearly communicate architecture trade-offs, investment options, and risk decisions.
• Strong record of managing architecture decisions and technical risk when modernizing live enterprise cloud platforms and introducing new capabilities, including AI where appropriate.