Application Architect-AI Integration

Introduction
A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.
Your Role And Responsibilities
As an Application Architect for AI Integration, this role is responsible for designing and governing end-to-end architectures that embed AI capabilities into enterprise applications and workflows. The Application Architect collaborates with stakeholders to translate business objectives into scalable designs, balancing vendor-managed services with custom development, and drives governance for AI integration.
Your Primary Responsibilities Will Include
Designing Architectures: defining reference patterns for AI services, models, and orchestration layers to integrate seamlessly with existing systems, APIs, and data platforms.
Collaborating with Stakeholders: translating business objectives into scalable, resilient, and policy-compliant designs, ensuring performance, security, compliance, and cost objectives are met.
Governing AI Integration: driving governance for prompts, guardrails, and lifecycle management, ensuring observability, privacy, and responsible AI principles are embedded from design through operations.
Preferred Education
Master's Degree
Required Technical And Professional Expertise
AI Architecture Design: Experience with designing end-to-end architectures that embed AI capabilities into enterprise applications and workflows, ensuring seamless integration with existing systems, APIs, and data platforms.
Technical Governance: Experience with driving governance for prompts, guardrails, and lifecycle management, ensuring observability, privacy, and responsible AI principles are embedded from design through operations.
Reference Pattern Development: Experience with defining reference patterns for AI services, models, and orchestration layers, including Retrieval-Augmented Generation (RAG) and hybrid reasoning.
Stakeholder Collaboration: Experience with collaborating with stakeholders to translate business objectives into scalable, resilient, and policy-compliant designs, balancing vendor-managed services with custom development.
AI Service Integration: Experience with integrating AI services, models, and orchestration layers with existing systems, APIs, and data platforms, meeting performance, security, compliance, and cost objectives.
Preferred Technical And Professional Experience
Advanced AI Concepts: Experience with emerging AI technologies and trends, such as explainable AI and edge AI, can be beneficial.
Cloud Native Services: Knowledge of cloud-native services and serverless architectures can be advantageous in designing scalable AI integrations.
Data Platform Integration: Familiarity with integrating AI services with various data platforms and APIs can be useful.

Work arrangement
No