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

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

Required Qualifications

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