Staff AI Engineer

Teradata; Hyderabad; Vor Ort; INR 2.500.000 - 5.000.000; Vollzeit

Zusammenfassung

Teradata is seeking a Staff AI Engineer to shape the LLM and agent infrastructure for its cloud and SaaS platform. You will own architecture, integrate hosted and self-hosted models, and build ModelOps pipelines with strong emphasis on security, observability and multi-tenant reliability.

This role offers collaboration with AI architects, ML engineers and product teams across India and Silicon Valley to translate customer use cases into scalable features, applying best practices in cloud-native

Qualifikationen

• 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 (e.g. 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.

Aufgaben

• 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.

Kenntnisse

Python

Cloud engineering

LLM integration

Kubernetes

CI/CD

Multi-tenant SaaS

Agentic AI

LangChain

Ausbildung

BS/MS/PhD in Computer Science, AI/ML

Tools

Terraform

Docker

Jobbeschreibung

(Candidates Preferred with Product Background with Tier 1 Educational Background)

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.

• 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.