AI Engineer - Agentic Systems

What you will do

• Build the agentic systems that power Teradata's next generation of AI-driven analytics - intelligent agents that reason over enterprise data, invoke Vantage capabilities as tools, and complete multi-step analytical tasks autonomously.
• Design and implement agent skills and plugins that extend the Teradata agent harness.
• Build evaluation frameworks to measure agent quality.
• Contribute to the reliability and performance of agent workflows running on Vantage.
• Enable customers to move from asking Teradata questions to delegating analytical tasks to AI agents.

Who you will work with

• Join a focused team of AI engineers within Teradata's AI Apps, Analytics, and UX Engineering organization.
• Work on the most technically ambitious part of the Teradata product roadmap.
• Report to the Senior Manager of AI Engineering.
• Collaborate closely with Staff AI Engineers, the Agent Platform team, and Product Management.
• Access Teradata's world-class data infrastructure.
• Opportunity to shape how enterprise agents work at scale.

What makes you a qualified candidate

• 3-6 years of software engineering experience with at least 2 years building agentic AI systems in production - not just RAG pipelines or chatbots.
• Hands-on experience implementing agent skills, plugins, or tools that extend an agent harness (Claude Code, OpenCode, Codex, OpenClaw, LangGraph, or equivalent).
• Proficiency in Python; Go experience is a strong advantage.
• Understanding of agent planning loops, tool-calling protocols, function calling APIs (OpenAI, Anthropic, or Google format), and prompt engineering for reliability.

What you will bring

• Open-source contributions to agent frameworks or LLM tooling.
• Experience evaluating and benchmarking agentic systems: building evals, golden datasets, and automated regression testing for agent behaviors.
• Familiarity with Teradata Vantage, SQL query optimization, or columnar analytics - or with a competing platform (Snowflake, Databricks, BigQuery) at depth.
• Experience with vector databases, embedding models, and hybrid retrieval for agent memory and context management.
• Strong written communication skills.
• A passion for how AI can unlock potential to help teams, customers, and communities achieve great things.