Senior Data Engineer - AI & Analytics Infrastructure

About the role

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 strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. This role is central to the success of a high-priority Agentic AI engagement—the quality, accessibility, and governance of data directly enable the AI and analytics use cases being built. You will work alongside AI architects and engineers to ensure the right data reaches the right systems in the right form.

Responsibilities

• Design, build, and maintain robust data pipelines that ingest, transform, and deliver high-quality data across the platform.
• Develop scalable architectures using Microsoft Fabric, Databricks, and/or Azure Synapse Analytics.
• Ensure pipelines are performant, reliable, and built to handle the scale and variability of enterprise data.
• Implement data transformation and orchestration workflows that feed AI models and analytics dashboards.
• Architect and maintain the underlying data infrastructure that supports AI and analytics use cases.
• Define and implement data lakehouse patterns, medallion architecture, and layered data models.
• Collaborate with AI engineers and architects to ensure data outputs are structured and accessible for model consumption.
• Manage and optimize data storage, compute, and processing environments for cost and performance.
• Implement data quality checks, validation frameworks, and monitoring to ensure trustworthy data outputs.
• Establish and enforce data governance standards including lineage tracking, cataloging, and access controls.
• Partner with stakeholders to document data assets and ensure discoverability across the platform.
• Build and operationalize AI orchestration pipelines using frameworks such as LangChain and LangGraph.
• Develop AI agents capable of tool calling, contextual retrieval, memory/state management, multi-agent coordination, and autonomous workflow execution.
• Implement MCP (Model Context Protocol) integration patterns to enable secure, modular interoperability between AI agents, enterprise systems, tools, and data sources.
• Support the industrialization of AI capabilities through reusable architecture patterns, standardized deployment frameworks, monitoring, testing, evaluation pipelines, and operational support models.
• Develop and integrate enterprise-grade APIs, vector databases, workflow platforms, and operational systems into AI-enabled business processes.
• Implement AI governance, security, logging, guardrails, and human-in-the-loop controls to support responsible and scalable AI adoption.
• Contribute to CI/CD, LLMOps/MLOps, and cloud-native deployment practices supporting enterprise-scale AI delivery.

Requirements

• 7+ years designing, developing, and deploying scalable AI applications leveraging LLMs, RAG architectures, and agentic AI workflows.
• Experience with agentic frameworks such as LangChain, LangGraph, AutoGen, Semantic Kernel, or CrewAI.
• Familiarity with vector databases (e.g., Azure AI Search, Pinecone, Weaviate) for RAG implementations.
• Knowledge of MLOps practices and CI/CD pipelines for AI model deployment and lifecycle management.
• Experience with enterprise integration patterns and connecting AI solutions to CRMs, ERPs, or data platforms.

Qualifications

Preferred Education: Master's Degree