Our Company: 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 Teradata’s 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 Teradata’s 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. • Integrate and serve LLMs, both hosted APIs and self-hosted open-weight models: model gateway, routing, caching, fallbacks, token budgets, latency and cost control. • Build ModelOps / LLMOps pipelines: model, prompt and agent registries and versioning, CI/CD with evaluation gates, canary releases and rollback, and quality and drift monitoring. • Build evaluation and observability frameworks for LLM and agent systems: offline evals, LLM-as-judge, tracing, regression suites, and quality, safety, latency and cost metrics. • Implement AI guardrails and responsible-AI controls: prompt-injection defense, PII handling, permissions, policy enforcement and audit trails for regulated industries. • Run AI services as SaaS on AWS, Azure and Google Cloud: tenant isolation, authentication and authorization, quotas, usage metering, SLOs, and production and on-call readiness. • 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 Teradata’s 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.    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 across India and 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 fit real business problems in regulated industries. • Cloud, SaaS and infrastructure teams responsible for deploying, evaluating and scaling AI workloads. This is a rare opportunity to shape foundational AI capabilities within a global, data-driven company. This is a deeply collaborative environment where technical innovation meets real-world application, where your ideas are not only heard but implemented to shape the next generation of data interaction.      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. #LI-PG1