AI Engineer

Position: Staff 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*
* As a Senior Staff AI Engineer, you will be a senior technical leader responsible for designing, evolving, and scaling Teradata's enterprise AI platforms and capabilities. This role goes beyond feature delivery-you will own architecture level decisions, influence AI platform strategy, and lead complex initiatives spanning agentic AI, LLM platforms, retrieval augmented generation (RAG), vector stores, and developer facing AI systems.

You will operate with a high degree of autonomy, partner closely with product and architecture leadership, and raise the technical bar across multiple teams.

Key Responsibilities

_Technical Leadership & Architecture_

+ Lead the design and evolution of large scale, distributed AI systems that power Teradata's AI platform and AI native products.

+ Own end-to-end architecture for critical AI capabilities such as agentic workflows, RAG pipelines, vector search, semantic retrieval, and AI orchestration frameworks.

+ Drive technical strategy and architectural consistency across multiple engineering teams.

_AI Systems & Platform Development_

+ Design and implement production grade AI systems using LLMs, embeddings, vector databases, and agent based architectures.

+ Build scalable, secure, and reusable platform services and APIs supporting AI workloads across the software development lifecycle.

+ Define and implement guardrails for reliability, safety, governance, and cost control in enterprise AI systems.

_Cross Functional Influence_

+ Partner with product management, architecture, research, and cloud platform teams to translate business requirements into scalable AI solutions.

+ Influence roadmap decisions by providing deep technical insight, tradeoff analysis, and long-term platform thinking.

+ Act as a technical escalation point for complex system design, performance, and reliability challenges.

_Quality, Performance & Operational Excellence_

+ Drive best practices for testing, observability, evaluation, and production readiness of AI systems.

+ Identify systemic performance bottlenecks and lead efforts to optimize distributed systems and AI pipelines.

+ Establish engineering standards that improve development velocity, quality, and operational resilience.

_Mentorship & Org Level Impact_

+ Mentor Senior and Staff level engineers, providing guidance on architecture, design reviews, and technical decision making.

+ Raise the overall engineering bar through design forums, technical reviews, and knowledge sharing.

+ Lead by example through hands on contributions to the most complex and business critical problems.

** Who You'll Work With*
* You will partner closely with product and architecture leadership, and raise the technical bar across multiple teams.

** What Makes You a Qualified Candidate*
* + Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.

+ 8+ years of experience building backend services, distributed systems, or data/AI platforms.

+ Strong proficiency in Java, Go, or Python, with experience building large scale services.

+ Deep understanding of distributed system design, scalability, fault tolerance, and cloud native architectures.

+ Proven experience designing and operating production systems with SQL and No

SQL data stores.

** What You'll Bring*
* +

Experience with LLMs, embeddings, vector databases, and AI orchestration frameworks.

+ Exposure to agentic AI patterns such as tool calling, planning, memory, and multi-step reasoning.

+ Experience building or operating AI systems in cloud environments (AWS, Azure, or GCP).

+ Familiarity with Kubernetes, Docker, CI/CD pipelines, and production-grade observability.

** Why We Think You'll Love Teradata*
* We…