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 Sr. Principal Architect of Cloud Platform Engineering, you will lead the evolution of Teradata's cloud deployment and platform
technologies into the agentic era—designing and executing the technical strategy for a cloud platform engineered from the ground
up to serve autonomous AI systems as first-class compute consumers. You will define long-term cloud architecture across AWS,
Azure, and GCP with multi-tenant and single-tenant models, agent-driven execution frameworks, and infrastructure primitives that
agentic systems require: sub-second APIs, persistent agent memory, agent-scoped identity, fine-grained cost controls, and
deterministic audit trails. Your platform decisions will directly enable Teradata's engineering teams to adopt AI-native development
practices where autonomous agents become core participants in software delivery, while your leadership directly impacts the ability
to scale globally, onboard agentic applications to production reliably, and operate infrastructure serving both human users and
agents with equal rigor.
WHAT SUCCESS LOOKS LIKE
Success means delivering a highly automated, secure, and resilient cloud platform architected natively for autonomous AI systems
and the engineering teams that deploy them:
Sub-second agent API latencies; dynamic scaling for agent micro-queries; agent-driven provisioning and self-healing
infrastructure; reliable agentic workload execution with <5% cost variance from workload unpredictability
Identity and cost control systems that govern both human and agent actors; observable, debuggable agent execution; per
execution cost tracking and circuit breaking; deterministic audit trails for agentic decision reasoning
Engineering teams shipping features authored, tested, and reviewed by agentic systems; measurable velocity and quality
improvements from autonomous coding agents; reduced manual toil in infrastructure management and code verification
Reduced provisioning times; strong SLA adherence for both human and agent workloads; high platform availability (>99.95%);
optimized cloud spend with per-execution controls; incident response accelerated by agent-driven diagnostics and remediation
WHO YOU'LL WORK WITH
Lead and mentor teams across Platform Engineering, DevOps, and SRE on agentic-era infrastructure patterns and practices
Partner with Product, Security, Finance, AI/ML platform, and LLM framework teams to align infrastructure capabilities with both
business and agentic workload requirements
Collaborate with cloud providers and internal architecture teams to drive consistency, innovation, and operational excellence at
scale
Work with engineering leadership to establish AI-native development practices that depend on your platform primitives (agent
identity, cost controls, observability)
Report into senior engineering leadership, shaping Teradata's cloud, SaaS, and agentic platform strategy
WHAT MAKES YOU A QUALIFIED CANDIDATE
Proven experience leading cloud platform, infrastructure, or SaaS engineering teams at scale, with deep expertise in cloud
native architectures (serverless, event-driven, agent-driven execution models)
Hands-on experience building and productionizing agentic applications at scale in production environments (agents writing
code, managing infrastructure, making autonomous decisions), with deep familiarity in agentic frameworks (LangGraph, Claude
API with tool_use, MCP servers, agent orchestration, memory management) and understanding how to architect infrastructure
for their unique demands (sub-second latency, persistent memory, execution cost controls)
Proven ability to architect for agentic workload patterns: understanding agent failure modes (hallucinations, cost drift, goal
misalignment) and designing guardrails, observability, cost controls, and deterministic audit trails into platform primitives;
balancing reliability, performance, security, and cost across diverse workloads from bulk analytical scans to high-frequencyagent micro-queries
WHAT YOU'LL BRING
Direct, hands-on experience building systems where autonomous agents operate as core decision-makers and infrastructure
consumers (not just code-assist tools); deep understanding of LLM capabilities and limitations in production—token budgets,
latency requirements, hallucination handling, determinism, reproducibility, cost predictability
Ability to architect for agent auditability and debugging: designing systems that produce interpretable agent reasoning trails,
enable reasoning replay, and establish accountability for agentic decisions; financial acumen for agent compute optimization—
understanding per-execution spend tracking, cost circuit breaking, and the unique cost profiles of agentic vs. traditional
workloads
Hands-on leadership experience with AWS, Azure, and/or GCP at scale; expertise in IaC, CI/CD, and platform automation
(Terraform, Jenkins, GitHub Actions, deployment orchestration); strong understanding of observability, incident management,
DR, and SLA-driven operations extended to non-deterministic agent workloads
A security-first mindset embedding IAM, agent identity, delegated authority, and intent-scoped governance into platform design;
ability to define KPIs across deployment frequency, provisioning time, latency, error rates, infrastructure efficiency, agent
execution cost per task, and agentic system reliability
Demonstrated success building and scaling high-performing teams through periods of significant technical and organizational
change; proven ability to guide engineering teams on agentic thinking—shifting from "AI as tool" to "AI as engineer"—with
corresponding trust, autonomy, and verification frameworks
Ability to articulate the transition to agentic infrastructure across technical and non-technical audiences; comfort with pioneering
new patterns where agentic infrastructure is nascent and requires novel solutions for cost control, identity, latency, and
auditability
WHY THIS ROLE MATTERS
Agentic systems are moving from research labs into production. Cloud platforms not designed for agent workloads will struggle with
their unique demands: sub-millisecond latency sensitivity, unpredictable concurrency, deterministic auditability, and per-execution
cost transparency.
Teradata has the opportunity to lead by building a cloud platform architected natively for the agentic era. This means infrastructure
primitives designed from day one for agent identity, memory, cost controls, and reasoning auditability. It means engineering
practices where autonomous agents become first-class team members in software delivery.
The person in this seat will define what it means to operate cloud infrastructure in the age of agentic compute—setting the technical
and operational standards that enable Teradata to scale AI-native engineering practices and reliably productionize agentic
applications at scale.
#LI-VB1
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.
