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About This Role
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 the Principal AI Architect for Teradata AI Studio, you will define the technical architecture of Teradata's end\-to\-end AI development environment — the platform where data scientists, ML engineers, and AI developers build, test, deploy, and monitor AI and agentic applications on top of Vantage.
You will set the architectural direction for how AI Studio integrates with Teradata Vantage's query engine, model registry, feature store, and agent harness. You will establish the patterns for how enterprise customers build trustworthy AI workflows — from data preparation through model deployment to agent\-driven automation — and ensure that AI Studio is the most capable, governed, and scalable AI development environment in the market.
This is a hands\-on technical leadership role. Success means shipping architectural decisions that other engineers can build on with confidence, customers adopting AI Studio at scale, and Teradata being recognized as the platform of choice for enterprise AI development.
Who You’ll Work With
You will be the senior technical voice for AI Studio within Teradata's AI Apps, Analytics, and UX Engineering organization. You will partner with the VP of Engineering for AI Platform, the Staff AI Engineers building AI Studio components, and the Product Management team to align architecture with product strategy.
This role has significant cross\-functional reach — you will engage with Teradata's Core Data Platform team on Vantage integration points, with the Security and Governance team on enterprise\-grade AI controls, and with key customers in design partner engagements to ensure AI Studio solves real problems at enterprise scale.
What Makes You a Qualified Candidate
- 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.
- Deep understanding of LLM integration patterns: RAG architectures, fine\-tuning pipelines, evaluation frameworks, and agent tool\-calling interfaces.
- Experience with enterprise data platforms (Teradata Vantage, Snowflake, Databricks, or equivalent) at sufficient depth to architect against their APIs, security models, and performance characteristics.
What You’ll Bring
- Experience building developer\-facing platforms — SDKs, APIs, or IDEs — that external developers adopt and extend.
- Familiarity with open\-source AI development tools: MLflow, Weights \& Biases, Hugging Face, LangChain, LangGraph, or comparable.
- Understanding of enterprise AI governance requirements: model lineage, data access controls, audit logging, and responsible AI guardrails.
- Experience with cloud\-native architecture (AWS, Azure, GCP) and containerized ML workloads (Kubernetes, Docker).
- Strong cross\-functional influence: you can drive alignment across engineering, product, and customer\-facing teams without formal authority.
- A portfolio of architectural decisions — RFCs, design docs, or open\-source work — that demonstrates your approach.
- A passion for how AI can unlock potential to help our teams, our customers, and our communities achieve great things.
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.
\#LI\-SK1
Pay Rate: $217,300\.00 \- $271,600\.00 \- $325,900\.00 Annually
Starting pay for the successful applicant will depend on geographic location, internal equity, job\-related knowledge, skills, and candidate experience. Sales roles will be eligible for commission payments tied to quota achievement. All other permanent roles will be eligible for one of our annual incentive plans, which are based on company financial attainment and individual performance. Employees in this position are also eligible to participate in the Company’s comprehensive benefits programs, which include healthcare, life and disability insurance plans, a 401(k)\-retirement savings plan, and time\-off programs. Specific details of these benefits, including eligibility criteria and plan options, will be provided during the hiring process and can be reviewed here: https://www.teradata.com/About\-Us/Careers/Benefits
Teradata is proud to be an equal opportunity 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. We welcome and encourage individuals from all backgrounds to apply and join our team, bringing their unique perspectives and experiences to help us innovate and grow. If you require accommodations during the interview process, please let your recruiter know and we will work with you to meet your needs.
Salary Context
This $217K-$325K range is above the 75th percentile for AI Architect roles in our dataset (median: $180K across 25 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,824 AI roles we're tracking, AI Architect positions make up 1% of the market. At Teradata, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $220,000 based on 92 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($271K) sits 23% above the category median. Disclosed range: $217K to $325K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Teradata AI Hiring
Teradata has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span CA, US, US. Compensation range: $325K - $325K.
Location Context
AI roles in Austin pay a median of $218,800 across 493 tracked positions. That's 9% above the national median.
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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