The AI-Native Solutions Architect bridges business strategy, product vision, and engineering execution to design and scale secure, maintainable software, data, and AI solutions. The role leads architecture, rapid product validation, AI-enabled engineering practices, and technical governance to improve delivery and create measurable business outcomes.

Responsibilities

Solution architecture: align end-to-end designs, technology choices, tradeoffs, roadmaps, and enterprise standards.
Data architecture and engineering: model data, guide pipelines and integrations, ensure governance and quality.
AI-native architecture: integrate LLMs, agents, RAG, and automation into scalable business solutions.
MVP development and product validation: lead prototypes, translate ambiguity, apply Lean discovery, accelerate feedback.
AI harness and productivity: create reusable prompts, agents, evaluations, and AI-assisted development workflows.
Technical leadership and governance: mentor teams, review designs, establish standards and reusable architectures.
Emerging technology research: assess AI, cloud, data, and engineering tools; recommend valuable technologies.

Required Qualifications

5+ years software engineering; 3+ years technical leadership or architecture experience.
Scalable cloud-native and distributed systems; microservices, event-driven architecture, Clean Architecture, DDD, CQRS.
Hands-on AWS, Azure, or GCP; cloud services, networking, security, scaling, cost optimization, containers, Kubernetes, CI/CD, DevOps, IaC, observability.
Enterprise data models; relational and non-relational databases, SQL, APIs, events, messaging, ETL/ELT, data pipelines.
Data governance, quality, lineage, integrity, security; architecture performance and maintainability.
Production-ready MVPs and prototypes; hands-on LLMs, AI agents, RAG, or workflow automation.
Leadership, communication, stakeholder management, and problem-solving skills.