AI Native Solutions Architect

We are looking for an AI-Native Solutions Architect to design, validate, and scale modern software, data, and AI-powered solutions. This role combines solution architecture, data architecture, rapid product development, AI engineering, and technical leadership to transform ideas into scalable business outcomes.

The ideal candidate can bridge business strategy, product vision, and engineering execution while leveraging AI to accelerate delivery, enhance team productivity, and create intelligent digital experiences. They are responsible for architecting secure, scalable, and maintainable systems that combine traditional software engineering, enterprise data architecture, and modern AI capabilities.

This role serves as a key driver of technology innovation, helping organizations adopt AI-native approaches to product development, engineering excellence, data-driven solutions, and digital transformation.

Responsibilities

• Define end-to-end solution and data architectures that align with business requirements, enterprise constraints, and strategic objectives.
• Design solution architectures, technology stacks, integration patterns, data models, data flows, cloud strategies, and implementation roadmaps for software, data, and AI-enabled systems.
• Ensure alignment of technical and data architecture decisions with enterprise architecture principles, tribe standards, and established technology platforms.
• Lead rapid MVP, prototype, and proof-of-concept initiatives to validate opportunities, reduce delivery risk, and accelerate time-to-market.
• Design and implement AI-powered solutions including agents, copilots, RAG architectures, workflow automation, and intelligent business applications.
• Build reusable AI harnesses, accelerators, prompt frameworks, and engineering workflows that improve productivity, quality, and AI adoption.
• Provide technical leadership to engineering teams, including Technical Leads and Data Engineers, ensuring solutions are scalable, secure, maintainable, observable, and cost-efficient.
• Review solution and data architecture designs to ensure compliance with security, performance, scalability, data integrity, and governance standards.
• Establish architecture standards, governance practices, reusable assets, and reference architectures that improve delivery consistency across projects.
• Research, evaluate, and recommend emerging AI, cloud, data, and engineering technologies that create business value and competitive advantage.

Qualifications

• Minimum 5 years of software engineering experience, with at least 3 years in a Technical Lead, Solutions Architect, Data Architect, or equivalent technical leadership role.
• Strong experience designing scalable cloud-native applications, distributed systems, and enterprise integration architectures.
• Hands-on experience architecting and deploying solutions on major cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP), including cloud-native services, networking, security, scalability, and cost optimization.
• Experience designing enterprise data architectures, including conceptual, logical, and physical data models.
• Strong understanding of relational and non-relational databases, SQL, data integration, ETL/ELT, and data pipeline architectures.
• Experience integrating enterprise systems through APIs, event-driven architectures, messaging platforms, and data exchange mechanisms.
• Knowledge of data governance, data quality, data lineage, data integrity, and data security practices.
• Experience building enterprise MVPs, prototypes, and production-ready solutions.
• Hands-on experience with AI technologies including LLMs, AI agents, workflow automation, or RAG architectures.
• Strong understanding of architectural patterns such as Clean Architecture, Microservices, Event-Driven Architecture, Domain-Driven Design (DDD), and CQRS.
• Experience with containers, Kubernetes, CI/CD, DevOps, Infrastructure as Code (Terraform or equivalent), and observability practices.
• Excellent leadership, communication, stakeholder management, and problem-solving skills.

Detailed Expectations

1. Solution Architecture

• Design end-to-end business and technical solutions that translate business requirements into scalable and maintainable architectures.
• Define application architectures, technology stacks, integration patterns, and implementation approaches.
• Evaluate build-versus-buy decisions and architectural tradeoffs based on business value, technical feasibility, and enterprise constraints.
• Ensure solution designs align with enterprise architecture principles, security requirements, and established technology standards.
• Conduct architecture reviews and provide technical governance throughout the solution lifecycle.

2. Data Architecture & Engineering

• Define data architectures that support enterprise applications, business processes, integrations, and reporting requirements.
• Design and review conceptual, logical, and physical data models, data flows, and data integration patterns across enterprise systems.
• Define integration architectures between core platforms, management systems, databases, APIs, and downstream applications.
• Guide Data Engineers in implementing data pipelines, data transformations, ETL/ELT processes, and data integration solutions.
• Ensure data consistency, integrity, quality, security, lineage, and governance across integrated systems.
• Review data architecture and engineering designs to ensure performance, scalability, maintainability, and alignment with enterprise and platform standards.
• Provide technical guidance on data-related decisions, ensuring appropriate architecture and engineering practices are followed.

3. AI-Native Solution Architecture

• Design end-to-end business and technical solutions that integrate AI capabilities into products, platforms, and enterprise workflows.
• Architect systems that combine traditional software components with LLMs, AI agents, RAG pipelines, and workflow automation.
• Evaluate tradeoffs between build, buy, and AI-assisted approaches.
• Define scalable architectures that support rapid experimentation and long-term growth.

4. MVP Development & Product Validation

• Lead the creation of MVPs and proof-of-concepts to validate business opportunities rapidly.
• Translate ambiguous business requirements into working prototypes and production-ready architectures.
• Apply Lean Startup and product discovery principles to reduce delivery risk.
• Design architectures that support iterative experimentation and fast market feedback.

5. AI Harness Engineering

• Design and maintain AI harnesses that enable engineers, analysts, and business teams to effectively utilize AI.
• Build reusable AI workflows, prompts, agents, evaluation frameworks, and engineering accelerators.
• Establish standards for prompt engineering, agent orchestration, AI testing, and model evaluation.
• Develop reusable AI assets and playbooks that increase organizational productivity.

6. Engineering Productivity & AI Transformation

• Drive adoption of AI-assisted software development practices.
• Establish AI-native development workflows utilizing coding assistants, agentic development tools, and automated testing systems.
• Define best practices for human-AI collaboration across the software delivery lifecycle.
• Continuously identify opportunities where AI can improve engineering velocity and quality.

7. Technical Leadership & Governance

• Provide architectural direction and technical guidance to Technical Leads, Data Engineers, and development teams.
• Guide engineering teams in implementing scalable, secure, and maintainable application and data solutions.
• Conduct solution and data architecture reviews and provide technical governance.
• Mentor engineers, technical leads, and architects on modern architecture and AI-native development approaches.
• Establish architecture standards, reusable reference architectures, and engineering best practices.
• Ensure technical and data decisions remain aligned with enterprise, tribe, and project architecture standards.

8. Emerging Technology Research

• Evaluate emerging AI models, frameworks, cloud services, data platforms, and engineering tools.
• Continuously assess advancements in AI, cloud, data, and platform technologies.
• Recommend new technologies that create strategic advantages for clients and internal teams.