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.

What You'll Do

The Office of the Chief Technology Officer (OCTO) is building the next generation of intelligent, agentic systems that leverage large language models (LLMs) to automate complex enterprise workflows. We seek a Research Engineer to design, build, and productionize multi-agent platforms that extend Teradata’s analytical capabilities into autonomous, AI-driven pipelines.

This role sits at the frontier of applied AI research and platform engineering. You will work directly with state-of-the-art LLMs, agentic orchestration frameworks, and Teradata’s data ecosystem to create robust, scalable systems that enable autonomous reasoning, decision-making, and action across enterprise environments.

Design and Build Multi-Agent Platforms

• Architect and implement multi-agent systems that coordinate specialized LLM-powered agents to solve complex, multi-step analytical and operational tasks
• Design agent orchestration patterns including task decomposition, inter-agent communication, tool use, memory management, and feedback loops
• Build scalable agentic pipelines that integrate with Teradata’s data platform, enabling agents to query, analyze, and act on enterprise data autonomously

LLM Inferencing and Optimization

• Design and manage LLM inference pipelines optimized for latency, throughput, and cost across cloud and on-premises deployments
• Evaluate, benchmark, and select appropriate foundation models (open-source and proprietary) for specific agentic tasks within the Teradata ecosystem
• Implement advanced prompting strategies including chain-of-thought, retrieval-augmented generation (RAG), and tool-augmented reasoning to maximize agent reliability and accuracy

Agentic SDK Development and Integration

• Build and extend internal agentic SDKs and frameworks that enable research and product teams to rapidly develop and deploy agent-based applications
• Integrate with leading agentic platforms and toolkits (e.g., LangChain, LlamaIndex, AutoGen, CrewAI, Anthropic Claude SDKs) and adapt them for enterprise-grade reliability
• Develop reusable agent components, tool connectors, and evaluation harnesses that accelerate the path from prototype to production

Bridge Research and Delivery

• Collaborate with research teams to translate cutting-edge LLM and agent research into production-ready platform capabilities
• Work with product and service organizations to embed agentic workflows into Teradata’s customer-facing solutions
• Communicate agentic system design, tradeoffs, and capabilities clearly to technical and non-technical stakeholders across multidisciplinary teams

Who You'll Work With

You will work with product and service organizations to embed agentic workflows into Teradata’s customer-facing solutions.

What Makes You a Qualified Candidate

• Deep hands-on expertise with large language model APIs and inference frameworks (e.g., OpenAI, Anthropic, Mistral, vLLM, Ollama, HuggingFace Transformers).
• Strong practical experience designing and building multi-agent systems using agentic SDKs and orchestration frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or equivalent.
• Proficiency in Python and experience building production-grade AI/ML services with clean, well-documented, testable code.
• Solid understanding of RAG architectures, vector databases (e.g., Pinecone, Weaviate, pgvector), and knowledge retrieval patterns.
• Experience with prompt engineering, LLM evaluation methodologies, and strategies for improving agent reliability and reducing hallucination.
• Familiarity with LLM inference optimization techniques including quantization, batching, caching, and model serving infrastructure.
• 5+ years of software engineering experience, with at least 2 years focused on LLM-based systems, agentic workflows, or applied AI research.
• Bachelor’s degree in Computer Science, Artificial Intelligence, or a related field, or equivalent demonstrated expertise.
• Proven track record designing and shipping multi-agent or LLM-powered systems into production environments.
• Experience collaborating across research and engineering teams to move from prototype to scalable, maintainable platform capability.
• Strong analytical and problem-solving abilities with meticulous attention to system reliability, agent behavior, and edge-case handling.

What You Will Bring

• Experience building or contributing to agentic SDK frameworks or open-source LLM tooling.
• Familiarity with Model Context Protocol (MCP) or similar standards for tool-augmented LLM systems.
• Background with reinforcement learning from human feedback (RLHF), fine-tuning, or model alignment techniques.
• Experience with distributed systems and high-availability infrastructure for LLM serving at scale.
• Knowledge of AI governance, safety frameworks, and responsible deployment practices for autonomous agent systems.
• Prior work on AI/ML products within data warehousing, analytics, or enterprise software environments.

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