Application Architect-Ai Integration

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.

Required Education:

Bachelor's Degree

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.

Years of Experience:
12 - 16