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Ignite the Future of AI at Teradata. At Teradata, we're not just managing data; we're unleashing its full potential. Our ClearScape Analytics™ platform and pioneering Enterprise Vector Store are empowering the world's largest enterprises to derive unprecedented value from their most complex data. We're rapidly pushing the boundaries of what's possible with Artificial Intelligence, especially in the exciting realm of autonomous and agentic systems. We’re building intelligent systems that go far beyond automation — they observe, reason, adapt, and drive complex decision-making across large-scale enterprise environments. As a member of our AI engineering team, you’ll play a critical role in designing and deploying advanced AI agents that integrate deeply with business operations, turning data into insight, action, and measurable outcomes.
Job Responsibility:
Design and implement autonomous AI agents for semantic search, text-to-SQL translation, and analytical task execution
Develop modular prompts, reasoning chains, and decision graphs tailored to complex enterprise use cases
Enhance agent performance through experimentation with LLMs, prompt tuning, and advanced reasoning workflows
Integrate agents with Teradata’s Model Context Protocol (MCP) to enable seamless interaction with model development pipelines
Build tools that allow agents to monitor training jobs, evaluate models, and interact with unstructured and structured data sources
Work on retrieval-augmented generation (RAG) pipelines and extend agents to downstream ML systems
Requirements:
8+ years of software engineering experience
5+ years focused on AI/ML, intelligent systems, or agent-based architectures
Deep understanding of software design principles and scalable architecture patterns
Strong experience with LLM APIs (e.g., OpenAI, Claude, Gemini) and agentic frameworks (e.g., AutoGen, LangGraph, AgentBuilder, CrewAI)
Proven ability to build complex multi-step workflows using prompt pipelines, tools, and adaptive reasoning
Proficiency in Python and experience with vector databases, API integration, and orchestration tools
Familiarity with agent evaluation metrics: correctness, latency, grounding, and tool use accuracy
Experience leading AI projects from inception to deployment in a production setting
Master’s or Ph.D. in Computer Science, AI, or a related field, or equivalent industry experience
Experience working with multimodal inputs, retrieval systems, or structured knowledge sources
Hands-on experience with prompt engineering, function-calling agents, RAG patterns, and evaluation harnesses
Prior work with Model Composition Protocol (MCP) or similar orchestration frameworks is a strong plus
Excellent cross-team communication and stakeholder engagement skills
Passion for shipping high-quality AI products that are safe, explainable, and valuable
Nice to have:
Experience working with multimodal inputs, retrieval systems, or structured knowledge sources
Hands-on experience with prompt engineering, function-calling agents, RAG patterns, and evaluation harnesses
Prior work with Model Composition Protocol (MCP) or similar orchestration frameworks