Technical Consultant-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 a Technical Consultant specializing in AI Integration, you will play a crucial role in implementing, configuring, and customizing AI-driven solutions that integrate seamlessly into enterprise applications and workflows. You will translate solution designs and architectural blueprints into working systems, ensuring that AI services, APIs, and orchestration layers are deployed securely, efficiently, and in compliance with organizational and regulatory standards.
Your Primary Responsibilities Will Include
Implement AI Solutions: Develop and deploy AI-driven solutions, translating architectural blueprints into working systems that meet organizational and regulatory standards. This involves configuring AI services, APIs, and orchestration layers to ensure seamless integration with enterprise applications and workflows.
Collaborate with Stakeholders: Work closely with architects, data engineers, and business stakeholders to validate requirements, deliver proofs of concept, and harden solutions for production. This requires effective communication and collaboration to ensure that solutions meet business needs and are delivered on time.
Develop Connectors and Pipelines: Design and develop connectors, build Retrieval-Augmented Generation (RAG) pipelines, and configure prompts and guardrails to support AI-driven solutions. This involves applying technical expertise to ensure that solutions are scalable, reliable, and efficient.
Optimize Performance: Implement observability, performance tuning, and cost optimization measures to ensure that AI-driven solutions operate efficiently and effectively. This requires ongoing monitoring and analysis to identify areas for improvement.
Ensure Compliance: Ensure that AI services, APIs, and orchestration layers are deployed securely and in compliance with organizational and regulatory standards. This involves applying knowledge of security and regulatory requirements to mitigate risks and ensure compliance.
Preferred Education
Master's Degree
Required Technical And Professional Expertise
AI Solution Implementation: Experience with developing and deploying AI-driven solutions, translating architectural blueprints into working systems that meet organizational and regulatory standards.
API Integration and Orchestration: Experience in configuring AI services, APIs, and orchestration layers to ensure seamless integration with enterprise applications and workflows.
Workflow Automation and LLMOps: Experience with workflow automation and LLMOps practices, focusing on reliability, scalability, and responsible AI principles throughout the delivery lifecycle.
Event-Driven Patterns and RAG Pipelines: Experience in designing and developing connectors, building Retrieval-Augmented Generation (RAG) pipelines, and configuring prompts and guardrails to support AI-driven solutions.
Security and Compliance: Experience in 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 with API integration, event-driven patterns, and workflow automation, with a focus on reliability, scalability, and responsible AI principles throughout the delivery lifecycle.
Knowledge of LLMOps Practices: Experience with workflow automation and LLMOps practices, focusing on reliability, scalability, and responsible AI principles throughout the delivery lifecycle.
Familiarity with Retrieval-Augmented Generation (RAG) Pipelines: Experience in designing and developing connectors, building RAG pipelines, and configuring prompts and guardrails to support AI-driven solutions.

Work arrangement
Hybrid