Senior AI engineer

Teradata Corporation (SE); Bengaluru; Hybrid; INR 1,500,000 - 3,000,000; Full time

Benefits offered by this job

Flexible work model

Inclusive environment

Focus on well-being

Job summary

A global data analytics firm is seeking a Senior AI Engineer to design and build vector store and retrieval infrastructure for enterprise-scale AI systems. This role involves working at the intersection of LLMs and distributed systems, optimizing semantic search, and developing end-to-end RAG pipelines. Candidates should have 3+ years of software engineering experience, a strong foundation in backend systems, and hands-on experience with vector databases. Join a culture that prioritizes innovation and collaboration in shaping the future of AI.

Qualifications

• 3+ years of software engineering experience with a strong focus on backend systems.
• Hands-on experience with vector databases or vector search systems.
• Strong understanding of embeddings and similarity search.
• 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.

Responsibilities

• Design and implement vector store capabilities integrated with analytics platform.
• Build end-to-end RAG pipelines including data ingestion and chunking strategies.
• Develop and optimize semantic search algorithms for enterprise workloads.
• Enable multimodal RAG and agent-based workflows; design agentic AI patterns with tool calling, planning, memory, and orchestration.
• Implement guardrails for safety, reliability, and governance; build RAG evaluation frameworks with relevance, faithfulness, accuracy, and cost metrics.

Skills

Python

Software engineering

Backend systems

Data chunking

Semantic search

LLM-powered applications

Performance optimization

Education

BS/MS/PhD in Computer Science, AI/ML, or a related field

Tools

Vector databases

Cloud platforms (AWS, Azure, GCP)

AWS / Azure / GCP

Job description

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.

Ignite the Future of AI at Teradata!

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 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.

Why We Think You’ll Love Teradata

We prioritize a people‑first culture because we know our people are at the very heart of our success. We embrace a flexible work model because we trust our people to make decisions about how, when, and where they work. We focus on well‑being because we care about our people and their ability to thrive both personally and professionally. We are committed to actively working to foster an inclusive environment that celebrates people for all of who they are.