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Introduction

At IBM Global Sales, we bring together innovation, collaboration, and expertise to help clients solve their most complex business challenges. Working across industries and geographies, you’ll partner with colleagues, clients, and partners to co-create solutions that drive digital transformation and lasting impact.Success in Global Sales is built on curiosity, empathy, and collaboration. You’ll connect technical understanding with strong people skills, building trusted relationships and shaping solutions that improve business and society. With world-class onboarding, continuous learning, and a supportive culture, IBM offers the tools and opportunities to grow your career. Join us and be part of a global team that’s passionate about driving innovation and making a difference.

Your role and responsibilities

IBM is seeking a visionary Data Solution Architect to lead the convergence of data engineering and artificial intelligence. In this role, you will architect the next generation of intelligent data platforms that power both traditional analytics and cutting-edge Generative AI workloads. You will design the "nervous system" of the enterprise—building AI-ready data fabrics, enabling Retrieval-Augmented Generation (RAG) patterns, and establishing LLMOps pipelines. Join a dynamic, fast-growing team to tackle the industry's most interesting engineering challenges at the intersection of data and AI.

AI-Ready Data Architecture & Strategy

• Architect modern data platforms designed explicitly for the AI era, including vector databases, embedding pipelines, and feature stores to support large language model (LLM) applications.
• Define strategies for integrating enterprise knowledge bases with LLMs using RAG (Retrieval-Augmented Generation) patterns to deliver accurate, context-aware AI solutions.
• Lead the design of "Data Fabrics" and "Data Meshes" that enable decentralized data ownership while maintaining governance—ensuring data is clean, governed, and accessible for AI model training and inference.
• Evaluate and recommend emerging technologies (e.g., vector search engines, embedding models) to future-proof the data landscape.

Generative AI & LLMOps Implementation

• Architect and oversee the deployment of end-to-end LLMOps (Large Language Model Operations) pipelines, including prompt management, model fine-tuning, versioning, and monitoring in production.
• Design solutions for grounding LLMs with private, real-time data to mitigate hallucinations and ensure regulatory compliance.
• Lead the development of Proof of Concepts (PoCs) that demonstrate the business value of Generative AI, transforming them into scalable, production-ready architectures.

Data Governance in the AI Era

• Establish and enforce governance frameworks specifically for AI assets, including model lineage, bias detection, explainability, and compliance with emerging AI regulations (e.g., EU AI Act).
• Architect solutions for secure data access and privacy preservation (e.g., data masking, differential privacy) to protect sensitive data used in AI training.
• Utilize modern data catalogs and AI governance tools to automatically discover, classify, and document data assets across hybrid cloud environments.

Collaboration & Technical Leadership

• Act as a bridge between business leaders, data scientists, and engineers, translating complex AI use cases into robust technical architectures.
• Work with project managers and product owners to align the AI product roadmap with data infrastructure capabilities.
• Mentor engineering teams on best practices for building scalable data pipelines and AI integrations.
• Create technical white papers and demonstrations to showcase IBM’s thought leadership in the AI and data space.

Innovation & Best Practices

• Stay at the forefront of the AI revolution, continuously researching advancements in LLMs, agentic AI, and real-time data processing.
• Maintain sandbox environments to rapidly prototype and evaluate new AI frameworks, embedding models, and data platforms.

Required education

Bachelor's Degree

Required technical and professional expertise

Required Skills & Experience

• Experience: 5+ years in data architecture, data engineering, or technical leadership, with a significant portion of recent experience focused on AI/ML initiatives.
• AI & Data Architecture: Deep expertise in designing data platforms (Data Warehouses, Data Lakes, Lakehouses) and a strong understanding of vector databases, embeddings, and RAG architecture patterns.
• Generative AI & LLMOps: Practical experience moving AI models to production; familiarity with LLMOps concepts (prompt engineering, model fine-tuning, LangChain, LlamaIndex) is highly desirable.
• Data Integration: Extensive experience with data integration (ETL/ELT), streaming data (Kafka), and API-based data access patterns to feed real-time AI models.
• Cloud & Infrastructure: Proficiency in architecting solutions on cloud platforms (AWS/Azure/GCP) and containerized environments (Kubernetes, OpenShift). Experience with AI/ML PaaS services (e.g., SageMaker, Azure ML, IBM watsonx) is a strong plus.
• Governance & Security: Solid understanding of data governance, model governance, and system security best practices in the context of AI.
• Communication: Excellent written and spoken English skills, with the ability to articulate the value of complex AI architectures to C-level executives and non-technical stakeholders.

Preferred technical and professional experience

Preferred Qualifications

• Hands-on experience with IBM watsonx platform or equivalent enterprise AI platforms.
• Experience implementing RAG-based applications using open-source frameworks (LangChain, LlamaIndex) and vector stores (Pinecone, Weaviate, Chroma, Milvus).
• Familiarity with Responsible AI principles, including bias detection and model explainability.
• Relevant certifications in cloud architecture or AI/ML.