Jobs at TeradataAster

• The Application Security integrates and support security at every phase of the software development lifecycle (SDLC) and work closely with developers to ensure applications are secure from inception through release. Given Teradata’s large portfolio of analytic applications, we are pushing the boundaries of security by automating source code analysis, and analytic security. Our expertise lies in deep technical understanding of security and our application security engineers within Teradata. The Application Security team is tasked with enabling software developers to build secure application and products through automating security (Shifting Left).

• Build components across the engine spine: SQL front end (tokenizer, parser, binder), logical/physical plan layer, rule- and cost-based optimizer, and operators (joins, aggregates, sort, scan).
• Build the storage substrate: Arrow in-memory format, slotted-page on-disk format with checksums, buffer pool, and a B-tree or LSM access method.

• Own the end-to-end technical architecture of Teradata's AI Governance Operating System — defining the standards and specifications for intake pipelines, tool evaluation workflows, and compliance monitoring automation that the broader team builds against
• Lead the engineering of sophisticated agentic workflows (using n8n, Claude APIs, or similar) th...

• Manage and support mission-critical, 24x7 public cloud environments across Azure and GCP for multiple customers.
• Execute risk assessments during task execution to ensure platform stability and minimize service impact.

• Build components across the engine spine: SQL front end (tokenizer, parser, binder), logical/physical plan layer, rule- and cost-based optimizer, and operators (joins, aggregates, sort, scan).
• Build the storage substrate: Arrow in-memory format, slotted-page on-disk format with checksums, buffer pool, and a B-tree or LSM access method.

• Own and champion the product vision, strategy, and roadmap for Teradata’s AI platform portfolio, including VantageCloud Lake, ClearScape Analytics, AI Unlimited, ModelOps, LLMOps, enterprise vector capabilities, and AI/ML lifecycle management
• Define platform capabilities that enable customers to build, test, deploy, monitor, and govern ...

• Develop and own the end‑to‑end pricing strategy for the AI Platform portfolio.
• Define pricing strategy for AI workloads, including model training, inference, vector search, and agentic orchestration.

• Own and evolve how compute infrastructure is provisioned and operates across heterogeneous environments, including bare metal, VMs, containers, and cloud platforms.
• Define and tune system-level behavior across CPU, memory, storage, and network, including how workloads are scheduled and resources are utilized.

• Own all phases of the agile software development lifecycle, from software design through customer support.
• Research and establish technical direction for complex feature development; perform functional and performance problem analysis.

• Own and evolve how compute infrastructure is provisioned and operates across heterogeneous environments, including bare metal, VMs, containers, and cloud platforms
• Define and tune system-level behavior across CPU, memory, storage, and network, including how workloads are scheduled and resources are utilized

• Drive long‑term improvements by identifying systemic gaps in memory behavior and influencing design, validation, and operational practices across the platform
• Own end‑to‑end resolution and prevention of classes of customer‑impacting issues related to memory pressure, allocation, and execution‑path behavior

• Define the end-to-end technical architecture of the Agentic Database data plane: Postgres engine configuration, storage model, WAL strategy, MVCC handling, extension framework, and the five Agent Services layered on top.
• Make the foundational architectural calls on shared-storage vs. local-storage, separation of co...

• Design, develop, and maintain high quality systems, services, and applications with an emphasis on scalability, reliability, and performance.
• Collaborate with cross-functional engineers and product partners to shape design and consistently deliver end to end features.

• Own and evolve how compute infrastructure is provisioned and operates across heterogeneous environments, including bare metal, VMs, containers, and cloud platforms
• Define and tune system-level behavior across CPU, memory, storage, and network, including how workloads are scheduled and resources are utilized

• Agent Memory — durable short- and long-term memory for conversations, session state, checkpoints, and shared context, enabling reliable multi-agent coordination and warm-start recovery.
• LLM Cache — semantic similarity caching that eliminates redundant LLM calls on repeated agent queries, reducing token costs and response latency at scale.

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.

• Working on a team of professionals, you will design, implement, test, deploy, administer, and continually improve software solutions to ensure system reliability and availability, mitigate operational risks, track system health, and improve mean-time-to-discover and mean-time-to-respond for operational issues.

• Lead end‑to‑end development of AI agents, from ideation and prototyping to production‑ready releases on unified agent platforms
• Drive architecture and standardization of core agent building blocks (LLM gateways, observability, knowledge bases, vector stores, evaluation frameworks).

• Lead the design and evolution of largescale, 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.

• 10+ years of software engineering experience, including 3+ years in a senior architect or principal engineer role with platform-wide technical scope.
• Demonstrated expertise designing AI/ML platforms or developer tools: model serving infrastructure, feature stores, experiment tracking, MLOps pipelines, or AI agent development environments.