Application Architect-AI Integration

IBM; Kolkata District; On-site; INR 2,400,000 - 4,800,000; Full time

Job summary

IBM India is seeking an Application Architect for AI Integration to design end-to-end architectures embedding AI into enterprise applications and workflows. You will collaborate with stakeholders to translate business objectives into scalable designs, drive governance for AI prompts and lifecycle management, and ensure security, privacy, and cost objectives are met across the solution.

The role focuses on seamless integration with existing systems, APIs, and data platforms, balancing vendor

Qualifications

• Design end-to-end architectures embedding AI into enterprise apps and workflows.
• Drive governance for prompts, guardrails, and lifecycle management.
• Collaborate with stakeholders to translate business objectives into scalable, policy‑compliant designs.

Responsibilities

• Design architectures: define reference patterns for AI services, models, and orchestration layers integrated with APIs and data platforms.
• Collaborate with stakeholders to translate business objectives into scalable, resilient designs meeting performance, security, and cost objectives.
• Govern AI integration: ensure observability, privacy, and responsible AI principles from design through operations.

Skills

AI Architecture Design

Technical Governance

Reference Pattern Development

Stakeholder Collaboration

AI Service Integration

Education

Bachelor's Degree

Master's Degree

Job description

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:

8 - 10