AI Architect

• Strong expertise across the full Software Development Lifecycle (SDLC).
• Hands-on experience with AI and Generative AI technologies and their application to software delivery processes.
• Deep understanding of modern software architecture patterns, distributed systems, microservices, and cloud-native solutions.
• Experience with cloud platforms such as Azure, AWS, or GCP.
• Strong knowledge of DevOps practices, CI/CD pipelines, infrastructure automation, and platform engineering.
• Experience defining enterprise-wide engineering standards, architecture governance, and best practices.
• Understanding of AI governance, security, compliance, and risk management principles.
• Proven track record of leading large-scale technology transformation initiatives.
• Strong knowledge of application security, data governance, and regulatory compliance requirements.
• Excellent stakeholder management, communication, and leadership skills.
• Fluent English, both written and spoken.

The AI Architect is the foremost technical authority for AI-driven software delivery practices. This role defines engineering strategy, architecture standards, AI governance implementation, and enterprise-scale adoption of AI across the software development lifecycle.

The AI Architect leads the transformation toward AI-native engineering practices while ensuring security, quality, traceability, and compliance are maintained throughout all stages of software delivery.

Drawing on emerging AI-Driven Lifecycle (AI-DLC) practices, the The AI Architect guides engineering teams in effectively leveraging AI across:

• Requirements engineering
• Solution and system design
• Software development
• Testing and quality assurance
• Deployment and release management
• Operations and continuous improvement

The role is responsible for establishing best practices, driving innovation, and ensuring that AI capabilities are embedded into engineering processes in a controlled, scalable, and business-aligned manner.

,[Define and drive the enterprise AI engineering strategy, standards, and technology roadmap., Establish and maintain architecture principles, engineering best practices, and AI governance frameworks., Lead the adoption of AI-native software development practices across the entire software delivery lifecycle., Provide technical leadership and guidance to engineering teams on the effective use of AI tools and platforms., Design and oversee scalable, secure, and compliant AI-enabled solutions and development processes., Ensure software quality, security, traceability, and regulatory compliance in AI-assisted development environments., Define standards for AI-supported requirements management, solution design, coding, testing, deployment, and operations., Evaluate emerging AI technologies, frameworks, and engineering methodologies to drive innovation and continuous improvement., Collaborate with senior stakeholders, architects, product leaders, and delivery teams to align engineering initiatives with business objectives., Mentor senior engineers and architects, fostering technical excellence and a culture of innovation., Monitor AI adoption, effectiveness, and risk management across engineering organizations., Drive enterprise-wide transformation towards AI-driven software delivery and operational excellence.] Requirements: AI, Security, Testing, LLM, RAG, CI/CD, Microservices, Kubernetes, Cloud platform, Azure, AWS, GCP, Jira, Confluence, SharePoint, OpenAI