T
Staff AI Engineer
Teradata
5 hours ago
Full-time
On-site
Southampton, New York, United States
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 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. #LI-NM1 Why We Think You’ll Love TeradataWe 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.
#J-18808-Ljbffr
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 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. #LI-NM1 Why We Think You’ll Love TeradataWe 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.
#J-18808-Ljbffr