Solution Architect, AI SDLC

A Solution Architect is the foremost technical authority for AI-driven software delivery practices. The role defines engineering strategy, architecture standards, AI governance implementation, and enterprise-scale adoption of AI throughout the software development lifecycle. This individual drives transformation towards AI-native engineering practices while ensuring security, quality, traceability, and compliance. Inspired by emerging AI-DLC practices, Solution Architect guides teams in leveraging AI across requirements, design, development, testing, deployment, and operations

About the Client

Our client is a large global enterprise operating across multiple markets, with a complex technology landscape and a strong focus on digital transformation and innovation. The organization is actively investing in modern cloud, data, and AI capabilities to enable scalable, secure, and highly automated solutions across its business.

About the Project

The project is focused on building an enterprise-grade AI Agent Platform from the ground up. The platform will provide a standardized foundation for developing, deploying, orchestrating, securing, and observing AI agents across multiple teams and use cases.

The initiative covers the full agent lifecycle and brings together agent orchestration, observability, security, governance, integrations, evaluation, and platform engineering. Engineers joining the project will have an opportunity to influence key architectural and technical decisions and contribute to a new platform rather than maintaining an existing solution.

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, AI, or equivalent experience.
• 12+ years of software engineering experience.
• 5+ years leading architecture or platform engineering initiatives.
• Deep expertise in cloud-native architecture and distributed systems.
• Strong understanding of modern AI/ML technologies, LLMs, agent frameworks, RAG, vector databases, and MLOps.

Technical Expertise

• Enterprise architecture
• AI engineering platforms
• LLM orchestration
• RAG and knowledge systems
• CI/CD and DevSecOps
• AI governance frameworks
• Microservices and event-driven architectures
• Kubernetes and cloud platforms (Azure/AWS/GCP)

Key Competencies

• Strategic technical leadership
• Innovation mindset
• Executive communication
• Systems thinking
• Mentorship and coaching
• Enterprise influence

Responsibilities:

Technical Leadership

• Define enterprise architecture and engineering standards for AI-enabled development.
• Create frameworks, reference architectures, and reusable platform capabilities.
• Act as the ultimate escalation point for complex technical decisions.

AI SDLC Strategy

• Lead adoption of AI-assisted software engineering practices.
• Establish standards for prompt engineering, agent-based development, AI testing, AI observability, and AI governance.
• Drive AI integration throughout the software development lifecycle.

Engineering Excellence

• Champion Domain-Driven Design (DDD), Behaviour-Driven Development (BDD), and Test-Driven Development (TDD).
• Define best practices for automated code generation, review, validation, and deployment.
• Establish engineering metrics and quality gates.

AI Governance & Risk

• Ensure responsible AI implementation.
• Define controls around security, privacy, bias, explainability, and compliance.
• Collaborate with architecture, security, legal, and governance teams.

Organizational Impact

• Mentor senior engineers and technical leads.
• Drive engineering capability development and innovation.
• Represent the organization in executive technical discussions.