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.

Location: Hybrid – Hyderabad, Pune and Bangalore, India 

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 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:

• Own the architecture and technical direction of Teradata’s vector store and retrieval stack.
• Define design patterns and best practices for RAG, semantic search, and agentic AI systems.
• Lead technical design reviews and influence cross-team architectural decisions.
• Identify scalability, performance, and reliability risks and drive solutions proactively.
• Design agentic AI patterns, including tool calling, planning, memory, and orchestration.
• Design and implement core vector store capabilities, including indexing strategies, storage layouts, retrieval algorithms, and APIs.
• 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.
• 8+ years of professional software engineering experience, including ownership of complex systems.
• Deep expertise in vector search, information retrieval, or semantic search systems.
• Proven experience designing and deploying production-grade RAG platforms.
• 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.
• Open-source contributions or published work in AI, IR, or GenAI.

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