Staff AI Agent Engineer - NLP

Our Company:

At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.

What You'll Do: Shape the Way the World Understands Data

We are looking for a Staff NLP & AI Agent Engineer to provide technical leadership in building scalable, production-grade AI agent systems. In this role, you will drive agent architecture, influence technical direction across teams, and ensure agent solutions meet high standards for quality, reliability, and maintainability. You will:

- Lead the architecture and design of intelligent AI agent systems, from concept through production.

- Define best practices for agent reasoning workflows, tool integration, and system interfaces.

- Drive the design of agent evaluation, benchmarking, and observability frameworks across teams.

- Identify systemic failure modes in agent behavior and guide long-term improvements.

- Collaborate with product, infrastructure, and data platform teams to align agent capabilities with business needs.

- Mentor senior and mid-level engineers on agent design, NLP techniques, and system tradeoffs.

- Influence technical roadmaps and contribute to strategic decisions related to AI agent platforms.

Who You'll Work With: Join Forces with the Best

You'll collaborate with a world-class team of AI architects, ML engineers, and domain experts at Silicon Valley, working together to build the next generation of enterprise AI systems. You'll also work cross-functionally with:

- Product managers and UX designers to craft agentic workflows that are intuitive and impactful.

- Domain specialists to ensure solutions align with real-world business problems in regulated industries.

- Infrastructure and platform teams responsible for training, evaluation, and scaling AI workloads.