Solutions Architect Software

The AI-Native Solutions Architect role focuses on designing, validating, and scaling AI-powered software solutions. This position combines solution architecture, rapid product development, and technical leadership to drive technology innovation and help organizations adopt AI-native approaches for enhanced productivity and digital transformation.

Roles & Responsibilities

• Design end-to-end business and technology solutions that align with stakeholder needs and strategic objectives.
• Define solution architectures, technology stacks, integration patterns, and implementation roadmaps for software and AI-enabled systems.
• Lead rapid MVP, prototype, and proof-of-concept initiatives to validate opportunities and accelerate time-to-market.
• Design and implement AI-powered solutions including agents, copilots, and workflow automation.
• Build reusable AI harnesses, accelerators, and engineering workflows to improve productivity and AI adoption.
• Provide technical leadership to engineering teams, ensuring solutions are scalable, secure, and maintainable.
• Establish architecture standards and governance practices to improve delivery consistency across projects.
• Research and recommend emerging AI, cloud, and engineering technologies that create business value.
  
  
  

• Minimum 5 years of software engineering experience, with at least 3 years in a Technical Lead, Architect, or equivalent leadership role.
• Strong experience designing scalable cloud-native applications and distributed systems.
• Hands-on experience architecting and deploying solutions on major cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Experience building enterprise MVPs, prototypes, and production-ready solutions.
• Hands-on experience with AI technologies including LLMs, AI agents, and workflow automation.
• Strong understanding of architectural patterns such as Clean Architecture, Microservices, and Domain-Driven Design (DDD).
• Experience with containers, Kubernetes, CI/CD, DevOps, and Infrastructure as Code.
• Experience with AI testing and model evaluation.
• Familiarity with Lean Startup and product discovery principles.
• Strong leadership and mentoring skills for guiding engineering teams.
• Ability to evaluate tradeoffs between build, buy, and AI-assisted approaches.
• Knowledge of best practices for human-AI collaboration across the software delivery lifecycle.