AI Solutions Architect

Role Overview

This role is designed for a practitioner who has evolved from deep experience in Artificial Intelligence into hands-on, production-grade software development using AI-assisted methodologies. The individual is expected to architect, build, and deliver robust, scalable products by leveraging AI not merely as a support tool, but as a core development paradigm.

In addition to technical excellence, this role carries a strong leadership mandate—to institutionalize AI-driven development practices and actively elevate the capabilities of the broader engineering team.

Key Responsibilities

• AI-Native Product Development
• Design and deliver end-to-end software solutions using AI-assisted development workflows.
• Translate business problems into scalable system architectures and working products.
• Own delivery from concept → prototype → production.
• AI-Assisted Engineering Practices
• Use advanced AI tools (LLMs, agents, code generation systems) to accelerate development while maintaining code quality and architectural integrity.
• Establish patterns for prompt engineering, agent orchestration, and reusable AI-driven workflows.
• Ensure generated code adheres to best practices in modularity, performance, and security.
• System Architecture & Design
• Define backend, frontend, and data architectures for modern applications (web, SaaS, enterprise systems).
• Design APIs, data models, and workflows optimized for AI-augmented systems.
• Integrate AI components (NLP, CV, predictive models) into production-grade systems.
• Engineering Governance
• Enforce code quality standards, version control discipline, testing strategies, and CI/CD pipelines.
• Review and refine AI-generated code to meet production standards.
• Establish guardrails for reliability, observability, and maintainability.
• Rapid Prototyping & Iteration
• Build functional prototypes at high velocity using AI tools.
• Iterate quickly based on stakeholder feedback and evolving requirements.
• Balance speed with long-term scalability and technical debt management.
• AI Strategy & Enablement
• 
  Define how AI can be systematically leveraged across engineering workflows.
• Evaluate and integrate emerging AI tools and frameworks into the development stack.
• Drive adoption of AI-native development practices across teams.

Leadership & Capability Building

• Team Enablement
• Mentor engineers in adopting AI-assisted development workflows effectively and responsibly.
• Conduct hands-on sessions, code walkthroughs, and live builds to demonstrate best practices.
• Enable teams to move from ad-hoc AI usage to structured, repeatable engineering approaches.
• Upskilling & Knowledge Transfer
• Design internal playbooks, templates, and reusable patterns for AI-driven development.
• Create documentation and training material to standardize practices across teams.
• Act as a multiplier—raising the overall productivity and capability of the engineering organization.
• Technical Leadership
• Lead by example through high-quality implementations and disciplined engineering practices.
• Influence architectural decisions and guide teams on trade-offs between speed and scalability.
• Foster a culture of experimentation balanced with accountability and production readiness.

Required Qualifications

• Experience
• 10+ years in AI / Machine Learning / Data Science or related domains.
• Recent, hands-on experience building production software using AI-assisted coding tools.
• Demonstrated track record of delivering real-world products (not just prototypes).
• Technical Expertise
• Strong proficiency in modern programming languages (e.g., JavaScript/TypeScript, Python, or similar).
• Experience with backend frameworks (Node.js, Express, FastAPI, etc.) and modern frontend stacks.
• Solid understanding of databases (SQL), APIs, and distributed systems.
• AI Engineering Capability

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• Deep familiarity with LLMs, prompt engineering, and agent-based systems.
• Experience integrating AI models into applications (APIs, pipelines, inference systems).
• Understanding of AI limitations, evaluation, and reliability considerations.
• Software Engineering Fundamentals
• Strong grasp of system design, scalability, and performance optimization.
• Experience with DevOps practices: CI/CD, containerization, cloud environments.
• Ability to write clean, maintainable, and testable code—even when AI-generated.

Preferred Qualifications

• Experience building internal AI tooling, developer platforms, or automation systems.
• Familiarity with multi-agent orchestration frameworks and workflow engines.
• Exposure to enterprise or government-grade systems with high reliability requirements.
• Prior experience in mentoring teams or leading engineering initiatives.

Key Traits

• Builder & Leader: Ships products while uplifting the team.
• AI Fluent: Uses AI as a core engineering multiplier with discipline.
• Teacher Mindset: Actively shares knowledge and builds team capability.
• Systems Thinker: Understands end-to-end architecture and trade-offs.
• Ownership Driven: Accountable for outcomes, not just outputs.

Success Criteria

• Deliver production-ready systems at significantly accelerated timelines using AI.
• Establish and scale AI-assisted development practices across teams.
• Measurably improve team productivity and engineering quality through upskilling.
• Create a self-sustaining engineering culture that effectively leverages AI.