Senior AI Engineer

Our Company:

At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.

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 Senior AI Engineer to play a key role in designing and building Teradata's vector store and retrieval infrastructure, powering RAG, multimodal AI, agentic workflows, and semantic search at enterprise scale.

This role is ideal for an engineer who thrives at the intersection of LLMs, information retrieval, and distributed systems, and wants to work on core platform capabilities, not just application demos.

You will:

• Design and implement vector store capabilities integrated with Teradata's analytics platform, including indexing, storage, retrieval, and query optimization.
• Build end-to-end RAG pipelines, including:
• Data ingestion and chunking strategies
• Embedding generation and lifecycle management
• Retrieval (dense, sparse, and hybrid search)
• Context assembly and prompt orchestration
• Develop and optimize semantic search algorithms and ranking strategies for enterprise workloads.
• Enable multimodal RAG (text, structured data, images, etc.) and agent-based workflows.
• Design agentic AI patterns, including tool calling, planning, memory, and orchestration.
• Implement guardrails for safety, reliability, and governance (hallucination mitigation, rounding, policy enforcement).
• Build and maintain RAG evaluation frameworks, including relevance, faithfulness, accuracy, and cost metrics.
• Collaborate with product, research, and platform teams to translate customer use cases into scalable features.
• Benchmark Teradata's vector store and RAG capabilities against industry alternatives (e.g., cloud and open-source solutions).
• Contribute to technical design reviews, architecture decisions, and long-term AI platform strategy.

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 at 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 align with real-world business problems in regulated industries.
• Infrastructure and platform teams responsible for training, evaluation, 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.
• 3+ years of software engineering experience with a strong focus on backend systems.
• Hands-on experience with vector databases or vector search systems.
• Practical experience building LLM-powered applications, especially RAG systems
• Strong understanding of:

• Embeddings and similarity search
• Data chunking and context optimization
• Dense vs sparse vs hybrid retrieval
• Semantic search and relevance ranking

• Proficiency in Python (and/or Java); experience with production-grade systems.
• Experience working with large-scale data and performance-sensitive systems.

Preferred Qualifications

• Experience with multimodal embeddings and retrieval.
• Familiarity with agent frameworks (e.g., LangChain, LangGraph, or equivalent).
• Experience implementing AI guardrails and evaluation frameworks.
• Exposure to cloud platforms (AWS, Azure, or GCP).
• Experience with distributed systems or analytics platforms.

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