Technical Consultant-AI Integration

Introduction

Build a long-term career at IBM Consulting by strengthening client relationships and delivering through close, global collaboration. In this role, you’ll work with leading organizations across industries to shape their hybrid cloud and AI strategies. Backed by strategic partners, IBM technology, and Red Hat, you’ll be equipped to drive meaningful transformation and help accelerate client outcomes. At IBM Consulting, curiosity is a key driver of success—you’ll be supported to question the status quo, investigate new concepts, and deliver inventive solutions that produce measurable results. Our growth-focused, empathetic culture supports your long-term career development, while recognizing and valuing your individual skills and experiences.

Your Role And Responsibilities

As a Technical Consultant specializing in AI Integration, you will implement, configure, and tailor AI-enabled solutions that integrate smoothly into enterprise applications and day-to-day workflows. You will convert solution designs and architectural blueprints into production-ready systems, ensuring that AI services, APIs, and orchestration layers are deployed securely, efficiently, and in alignment with organizational and regulatory requirements.

Your Primary Responsibilities Will Include

• Implement AI Solutions: Build and deploy AI-driven solutions by turning architectural blueprints into functional systems that satisfy organizational and regulatory standards. This includes configuring AI services, APIs, and orchestration layers to enable seamless integration with enterprise applications and workflows.
• Collaborate with Stakeholders: Partner with architects, data engineers, and business stakeholders to confirm requirements, deliver proofs of concept, and transition solutions into production-ready offerings. Strong communication and teamwork are required to ensure solutions align with business needs and are delivered on schedule.
• Develop Connectors and Pipelines: Create connectors, develop Retrieval-Augmented Generation (RAG) pipelines, and configure prompts and guardrails to enable AI-driven capabilities. Apply technical expertise to ensure the resulting solutions are scalable, dependable, and efficient.
• Optimize Performance: Establish observability, perform performance tuning, and execute cost optimization to ensure AI-driven solutions run effectively and efficiently. Continuously monitor and analyze results to identify and implement improvements.
• Ensure Compliance: Deploy AI services, APIs, and orchestration layers securely while meeting organizational and regulatory compliance expectations. Use security and regulatory knowledge to reduce risk and maintain compliance.
  
  

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• AI Solution Implementation: Hands-on experience developing and deploying AI-driven solutions, converting architectural blueprints into working systems that meet organizational and regulatory standards.
• API Integration and Orchestration: Experience configuring AI services, APIs, and orchestration layers to ensure smooth integration with enterprise applications and workflows.
• Workflow Automation and LLMOps: Experience with workflow automation and LLMOps practices, ensuring reliability, scalability, and responsible AI principles throughout the delivery lifecycle.
• Event-Driven Patterns and RAG Pipelines: Experience designing and building connectors, creating Retrieval-Augmented Generation (RAG) pipelines, and configuring prompts and guardrails for AI-driven solutions.
• Security and Compliance: Experience deploying AI services, APIs, and orchestration layers securely and in compliance with organizational and regulatory standards.
  
  

Preferred Technical And Professional Experience

• Strong Hands-on Expertise in API Integration: Experience integrating APIs, applying event-driven patterns, and enabling workflow automation, with an emphasis on reliability, scalability, and responsible AI principles across the delivery lifecycle.
• Knowledge of LLMOps Practices: Experience applying workflow automation and LLMOps practices with a focus on reliability, scalability, and responsible AI principles throughout the delivery lifecycle.
• Familiarity with Retrieval-Augmented Generation (RAG) Pipelines: Experience designing and developing connectors, building RAG pipelines, and configuring prompts and guardrails to support AI-driven solutions.