Senior Data Engineer - AI & Analytics Infrastructure

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. We are seeking an experienced Data Engineer to support the design and scaling of data pipelines and infrastructure for a high-priority Agentic AI engagement. This role is central to the success of the program — the quality, accessibility, and governance of data directly enables the AI and analytics use cases being built.

You will work alongside AI architects and engineers to ensure that the right data reaches the right systems in the right form. The client is looking for someone with strong hands-on experience across modern data platforms who can operate with confidence and deliver at pace. What You'll Do Data Pipeline Design & Development • 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 Data Infrastructure & Architecture • 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 Data Quality & Governance • 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. *This role can be performed from anywhere in the United States* * 7+ years designing, developing, and deploying scalable AI applications leveraging LLMs, RAG architectures, and agentic AI workflows. * 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. 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 United States Data & Analytics Hybrid Professional Multiple Cities (0147) International Business Machines Corporation