Note: The job is a remote job and is open to candidates in USA. IBM Consulting helps organizations advance their hybrid cloud and AI initiatives through collaboration, technology, and industry expertise. The Senior Data Engineer will design and scale data pipelines and infrastructure for an Agentic AI engagement, ensuring data quality, accessibility, governance, and performance for AI and analytics use cases.
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
Skills
- Master's Degree
- 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
Benefits
- This role can be performed from anywhere in the United States
- Long-term career development
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