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Open nowPosted yesterday

AI Architect – Generative & Agentic AI

IBM2,541 open roles

Where
Multiple Cities
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Your applicationOpen nowAI Architect – Generative & Agentic AIIBM · Multiple Cities

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The clock on this job

Early applications get read.

9.6% of postings close within 7 days. Measured by our own scanner across the market. IBM postings stay open a median of 7 days.

Share of postings closed within
  1. 1.9%1 day
  2. 3.9%3 days
  3. 9.6%7 days
  4. 15.6%14 days
  5. 33.9%30 days
This job: posted yesterday

IBM median: 7 days open

The posting

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.

The AI Architect will lead the architecture and technical direction of enterprise agentic AI initiatives for our client, driving scalable and governed AI solutions across Google Gemini, IBM Enterprise Advantage, SAP AI, Salesforce Einstein, and Databricks platforms. This role combines hands-on architecture leadership with enterprise AI governance and cross-platform orchestration expertise in a highly strategic client environment.

Your primary responsibilities will include:

• Design and architect enterprise agentic AI solutions using Google Gemini, Vertex AI, Agent Builder, and IBM Enterprise Advantage platform services.

• Define multi-agent and agent-to-agent orchestration patterns across SAP AI (BTP/Joule), Salesforce Einstein, Databricks, and enterprise systems.

• Lead architecture and implementation of long-running AI workflows including task coordination, state management, memory handling, tool use orchestration, and workflow resiliency.

• Build enterprise AI integration patterns leveraging APIs, event-driven architecture, microservices, and cloud-native services on GCP.

• Develop Retrieval-Augmented Generation (RAG) and enterprise knowledge solutions using Databricks data products and enterprise data platforms.

• Implement AI governance, guardrails, safety filters, access controls, auditability, and compliance mechanisms aligned to regulated utility industry requirements.

• Define AI solution standards, reusable architecture patterns, observability controls, and operational governance for production AI systems.

• Collaborate with client stakeholders, business teams, AI engineers, platform teams, and enterprise architects to align AI architectures with business objectives.

• Provide technical leadership and architecture governance for the Romania-based AI engineering team, including design reviews, implementation guidance, and engineering standards.

• Support AI solution estimation, roadmap definition, technical risk management, and production readiness reviews.

• Strong hands-on experience with Google Cloud Platform AI services including Vertex AI, Gemini API, Agent Builder, AI Studio, and related GCP AI capabilities.

• Experience designing and implementing agentic AI systems including multi-agent coordination, tool-calling patterns, planning workflows, memory/state management, and autonomous orchestration.

• Experience integrating enterprise AI ecosystems across SAP AI (BTP/Joule), Salesforce Einstein and Databricks.

• Deep understanding of enterprise AI architecture patterns, cloud-native design, APIs, event-driven systems, and scalable distributed architectures.

• Experience implementing AI governance controls including content safety, prompt filtering, model guardrails, governance policies, secure access patterns, and auditability.

• Strong Python development experience and familiarity with enterprise integration frameworks and AI orchestration tooling.

• Experience working within regulated environments such as utilities, energy, financial services, or healthcare preferred.

• Strong stakeholder communication and technical leadership skills with experience guiding distributed engineering teams.

• Google Cloud Professional certifications and AI/ML certifications preferred.

• Experience with enterprise AI platform modernization and production AI operations.

• Familiarity with MLOps / LLMOps practices including monitoring, evaluation, observability, and lifecycle management.

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