Our Company

At Teradata, we believe that people thrive when empowered with better information. That’s why we built the most complete cloud analytics and data platform for AI. By delivering harmonized data, trusted AI, and faster innovation, we uplift and empower our customers—and our customers’ customers—to make better, more confident decisions. The world’s top companies across every major industry trust Teradata to improve business performance, enrich customer experiences, and fully integrate data across the enterprise.

We are looking for a visionary and technically deep AI Architect to lead the design and delivery of enterprise-grade artificial intelligence solutions. This is a senior strategic role for someone who brings extensive hands-on experience across the full AI landscape — from Large Language Models (LLMs), Generative AI, and MCP servers, to machine learning pipelines, AI agent frameworks, and responsible AI governance. You will define the AI architectural vision, guide engineering teams, and ensure AI capabilities are embedded into the organization's products and platforms with scalability, security, and trustworthiness at their core. 

What You'll Do 

• Define and own the enterprise AI architecture strategy, establishing standards, reference architectures, and technology roadmaps for AI/ML adoption. 

• Architect and oversee the design of LLM-powered applications including RAG pipelines, AI agents, and agentic workflow systems. 

• Design and govern MCP server implementations, enabling structured, context-aware interactions between LLMs and enterprise data sources. 

• Lead the evaluation and selection of AI/ML platforms, LLM providers (OpenAI, Anthropic, Azure OpenAI, Google Gemini, open-source models), and supporting infrastructure. 

• Architect prompt engineering frameworks, fine-tuning pipelines, and model evaluation strategies to ensure LLM output quality, accuracy, and reliability. 

• Design AI orchestration layers using frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or custom agentic architectures for multi-step reasoning workflows. 

• Establish AI data pipelines for model training, embeddings generation, vector database management, and real-time inference serving. 

• Define and enforce responsible AI practices including bias detection, explainability, hallucination mitigation, content safety guardrails, and regulatory compliance. 

• Collaborate with engineering leads, data scientists, and product teams to embed AI capabilities into existing platforms and new product initiatives. 

• Mentor and upskill engineering teams on AI/ML technologies, architectural patterns, and emerging developments in the AI ecosystem. 

• Stay at the forefront of AI research and industry developments, identifying opportunities to apply new techniques and technologies to enterprise challenges. 

Who You'll Work With 

• Engineering Leads & Development Teams — to translate AI architectural vision into implementable designs and guide technical delivery across AI-powered products. 

• Data Scientists & ML Engineers — to align on model selection, training strategies, evaluation frameworks, and production deployment practices. 

• Product & Program Managers — to shape AI product roadmaps, define feasibility boundaries, and communicate AI capabilities to business stakeholders. 

• Platform & DevOps Teams — to architect scalable AI infrastructure, model serving pipelines, and MLOps workflows on cloud or on-premise environments. 

• Security & Compliance Teams — to ensure AI systems adhere to data privacy regulations, enterprise security policies, and responsible AI governance standards. 

• Executive & Technical Leadership — to advise on AI investment decisions, communicate architectural trade-offs, and present the strategic AI vision. 

• External AI Vendors & Partners — to evaluate platforms, negotiate technical capabilities, and maintain awareness of the broader AI vendor ecosystem. 

What Makes You a Qualified Candidate 

• 10+ years of experience in software engineering or technology architecture, with at least 4 years focused on AI, ML, and Generative AI solutions at enterprise scale. 

• Deep hands-on expertise with Large Language Models (LLMs) — including model selection, prompt engineering, fine-tuning (LoRA, PEFT), and evaluation techniques. 

• Proven experience designing and implementing MCP servers and structured context management strategies for LLM applications. 

• Strong working knowledge of Generative AI frameworks and orchestration tools — LangChain, LlamaIndex, LangGraph, AutoGen, CrewAI, or equivalent agentic platforms. 

• Hands-on experience with RAG architectures, vector databases (Pinecone, Weaviate etc), and embedding models. 

• Proficiency in Python-based AI/ML development, including libraries such as HuggingFace Transformers, PyTorch, TensorFlow, scikit-learn, and OpenAI SDK. 

• Experience with AI cloud platforms and services — Azure OpenAI, AWS Bedrock, Google Vertex AI, or equivalent enterprise AI infrastructure. 

• Solid understanding of MLOps practices including model versioning, experiment tracking (MLflow, Weights & Biases), CI/CD for ML, and model monitoring in production. 

• Familiarity with AI agent design patterns, multi-agent systems, tool-use frameworks, and autonomous workflow orchestration. 

• Knowledge of responsible AI principles, AI safety practices, regulatory landscape (EU AI Act, GDPR), and enterprise AI governance frameworks. 

• Strong foundation in software architecture patterns, distributed systems, API design, and cloud-native infrastructure (Docker, Kubernetes, microservices). 

What You'll Bring 

• Architectural Vision — The ability to think across the full AI stack — from raw model capabilities to production-grade enterprise systems — and define coherent, scalable architectures. 

• LLM & GenAI Depth — First-hand expertise with the latest LLM technologies, MCP patterns, agentic frameworks, and prompt engineering techniques that go beyond surface-level familiarity. 

• Strategic Thinking — The capacity to align AI architectural decisions with long-term business strategy, balancing innovation with pragmatism and enterprise constraints. 

• Thought Leadership — A recognized ability to shape AI direction within the organization, contribute to technical communities, and stay ahead of rapid advancements in the AI field. 

• Mentorship & Enablement — A genuine passion for upskilling engineering teams on AI technologies, instilling architectural discipline, and building internal AI capability. 

• Responsible AI Commitment — A principled approach to building AI systems that are fair, explainable, secure, and compliant with ethical and regulatory standards. 

• Communication Excellence — The ability to translate highly complex AI concepts into clear, compelling narratives for diverse audiences — from engineers to executives. 

• Ownership & Influence — A proactive, accountable style that drives AI initiatives from concept through production while influencing stakeholders at all levels. 

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