Senior AI Data Engineer

Senior AI Data Engineer Our Company

At Teradata, we believe that people thrive when empowered with better information. That’s why we built the most complete cloud analytics and data platform for AI. By delivering harmonized data, trusted AI, and faster innovation, we uplift and empower our customers—and our customers’ customers—to make better, more confident decisions. The world’s top companies across every major industry trust Teradata to improve business performance, enrich customer experiences, and fully integrate data across the enterprise.

As the recognized leader in data and analytics, Teradata is all about empowering high-impact business outcomes to unleash the potential of great companies. We focus every day on helping customers build lasting analytic capabilities and drive differentiated value through our flexible delivery of analytics at scale on an agile data foundation, now enhanced with cutting-edge AI, Generative AI, and Agentic AI capabilities.

Our mission is to build the world-class AI and data ecosystem for the industry leader in mega-scale Analytics platforms. Teradata is looking for senior engineers who can bridge the gap between data infrastructure and artificial intelligence. We need innovators who can architect robust data pipelines while building intelligent systems including autonomous AI agents, multi-agent systems, and AI-powered applications that can reason, plan, and take actions to solve complex business problems.

The Senior AI Data Engineer will architect and build both the data infrastructure and intelligent AI systems that power enterprise analytics, with primary focus on:

Design and build scalable data pipelines for batch and real-time processing using Teradata Vantage and enterprise data platforms. Develop integration solutions acquiring data from multiple sources, transforming it into analytics-ready formats optimized for AI/ML workloads. Leverage Teradata's advanced SQL, in-database analytics, and parallel processing. Create feature engineering pipelines and feature stores for ML operations. Implement data versioning and lineage tracking.

Design autonomous AI agents that perceive, reason, plan, and execute complex tasks with minimal human intervention. Develop agentic workflows using Lang Chain, Lang Graph, n8n, and orchestration tools. Build multi-agent systems where specialized agents collaborate through orchestration frameworks, task decomposition, and communication protocols. Create workflow automation with visual and code-based builders. Implement feedback loops, self-improvement mechanisms, and human-in-the-loop supervision. Deploy Model Context Protocol (MCP) for agent-to-agent and agent-to-system communication.

Build applications integrating LLMs, generative AI, and agentic capabilities with enterprise data systems. Design natural language interfaces for data operations, querying, and analytics. Create AI copilots that understand context, query databases, generate reports, and execute multi-step workflows. Develop tool-using agents that interact with APIs, databases, and external systems. Implement AI-driven automation for analysis, insight generation, and decision support.

Architect LLM solutions using foundation models and fine-tuned variants. Apply prompt engineering, few-shot learning, and chain-of-thought reasoning. Build retrieval-augmented generation (RAG) systems connecting LLMs with enterprise data using vector databases. Design hybrid search combining semantic and keyword approaches. Develop data ingestion for vector databases including chunking, embedding generation, and metadata management. Optimize inference with caching strategies.

Implement data management including lineage tracking, metadata management, quality monitoring, and cataloging. Build governance frameworks ensuring privacy, security, and compliance. Design data models for analytical and operational workloads. Create access controls and audit logging for AI applications.

Design…