Staff AI Engineer

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.

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