AI Native Product Architect

We are looking for an AI-Native Product Architect to design, validate, and scale modern software and AI-powered solutions. This role combines solution 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 with modern AI capabilities.

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

Responsibilities

Design end-to-end business and technology solutions that align with stakeholder needs, enterprise constraints, and strategic objectives.

Define solution architectures, technology stacks, integration patterns, cloud strategies, and implementation roadmaps for software and AI-enabled systems.

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, ensuring solutions are scalable, secure, maintainable, observable, and cost-efficient.

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, 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), including cloud-native services, networking, security, scalability, and cost optimization.

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

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

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.

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.

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.

Technical Leadership

Guide engineering teams in implementing scalable, secure, and maintainable solutions.

Conduct architecture reviews and provide technical governance.

Mentor engineers, technical leads, and architects on AI-native development approaches.

Establish architecture standards and reusable reference architectures.

Emerging Technology Research

Evaluate emerging AI models, frameworks, 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.