Staff AI Engineer

Senior and Staff AI Engineer

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

Teradata is building the next generation of AI-native analytics, enabling customers to deploy production-grade Generative AI systems directly where enterprise data lives. We are looking for a Staff AI Engineer to play a key role in designing and building the LLM, agent and ModelOps capabilities of Teradatas cloud and SaaS platform, taking them from prototype to secure, observable, multi-tenant production services.

This role is ideal for an engineer who thrives at the intersection of LLMs, agentic systems and cloud-native engineering, and wants to work on core platform capabilities, not just application demos.

You will:

Own the architecture and technical direction of Teradatas agentic AI platform, delivered as a secure, multi-tenant, cloud-native SaaS service.

Design and build agent runtimes and harnesses: tool calling, planning, memory, context management, multi-agent orchestration, sandboxed execution and human-in-the-loop controls.

Design, build and secure MCP servers and clients, and other agent and tool protocols, that expose Teradata capabilities to agents through governed, well-documented APIs.

Drive scalability, performance, reliability and cost efficiency of AI workloads, using Kubernetes and infrastructure-as-code.

Where use cases call for it, apply RAG, vector search and semantic retrieval patterns, including Teradatas vector store.

Lead technical design reviews and influence cross-team architectural decisions; identify risks early and drive solutions.

Collaborate with product, research and platform teams to turn customer use cases into scalable features, and benchmark against cloud and open-source alternatives.

Minimum Requirements

BS/MS/PhD in Computer Science, AI/ML, or a related field.

8+ years of professional software engineering experience, including ownership of complex production systems, and 2+ years building LLM-based applications or platforms in production.

Strong cloud engineering on AWS, Azure or Google Cloud: Kubernetes and containers, infrastructure-as-code (for example Terraform), and CI/CD.

Experience building and operating SaaS services: multi-tenancy, API design, authentication and authorization (OAuth/OIDC), observability, SLOs and reliability engineering.

Hands-on LLM experience: prompt and context engineering, function and tool calling, structured outputs, and working with both commercial and open-weight models.

Experience designing agentic systems: planning, memory, orchestration and custom agent harnesses, plus familiarity with agent frameworks such as LangGraph, LangChain or equivalent.

Experience with ModelOps / LLMOps: deploying, versioning, evaluating and monitoring models, prompts and agents in production.

Working knowledge of methods for evaluating LLM and agent quality, safety, latency and cost.

Proficiency in Python (and/or Java or Go), with experience shipping production-grade systems.

Experience with large-scale data and performance-sensitive distributed systems.

Preferred Qualifications

Experience with LLM inference and serving optimization (for example vLLM or Triton, quantization, GPU scheduling).

Experience with fine-tuning and post-training of open-weight models (for example LoRA, preference optimization, distillation).

Experience implementing AI guardrails, red-teaming and security controls for agents, such as prompt-injection defense.

Familiarity with agent-to-agent protocols and tracing standards such as A2A and OpenTelemetry for GenAI.

Experience with analytics platforms, data warehouses and SQL-based workloads.