Senior AI Engineer

Teradata; Telangana; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

Teradata is seeking a Senior AI Engineer to shape AI-native analytics, focusing on vector store and retrieval infrastructure. You will design and implement systems that enhance enterprise AI capabilities.

Your role involves collaborating with top AI architects and ML engineers while developing solutions that address real-world business challenges. Ideal candidates possess a strong background in software engineering, vector databases, and LLM technologies.

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.

Responsibilities

• Design and implement vector store capabilities integrated with Teradata's analytics platform.
• Build end-to-end RAG pipelines and optimize semantic search algorithms.
• Collaborate with product, research, and platform teams.

Skills

Software engineering

Vector databases

Large-scale data management

Python

LLM-powered applications

Education

BS/MS/PhD in Computer Science, AI/ML

Tools

AWS

Azure

GCP

Job description

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.

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.

Teradata invites all identities and backgrounds in the workplace. We work with deliberation and intent to ensure we are cultivating collaboration and inclusivity across our global organization. We are proud to be an equal opportunity and affirmative action employer. We do not discriminate based upon race, color, ancestry, religion, creed, sex (including pregnancy, childbirth, breastfeeding, or related conditions), national origin, sexual orientation, age, citizenship, marital status, disability, medical condition, genetic information, gender identity or expression, military and veteran status, or any other legally protected status.