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About This Role
Overview:
The Enterprise Architect – Data & AI enablement provides enterprise architecture leadership across business strategy, technology strategy, enterprise information assets, data governance, and AI-enabled business capabilities. The role helps REI make better long-term business and technology decisions by defining enterprise architecture direction, shaping future-state capabilities, guiding technology investments, and enabling coherent decision-making across business platforms.
This role serves as the primary Enterprise Architecture leader for Enterprise Data Architecture, Enterprise Information Architecture, AI Architecture, and Data Governance. It is accountable for setting enterprise direction, shaping architecture guardrails, and ensuring these domains are integrated into REI’s broader business, platform, security, privacy, and technology strategies.
The architect partners with business leaders, product leaders, engineers, architects, data teams, security, privacy, legal, risk, and technology executives to influence decisions affecting REI’s future business and technology landscape. The role balances strategic thinking with practical implementation guidance across emerging capabilities, enterprise platforms, and the capability domains most dependent on governed data, AI, and enterprise information architecture.
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Responsibilities: Key Responsibilities
- Enterprise Architecture Strategy and Roadmaps
- Define and evolve enterprise architecture strategy, principles, standards, reference architectures, and target-state roadmaps aligned to business strategy and long-term outcomes.
- Partner with business and technology leaders to align technology investments with business priorities, enterprise principles, platform strategy, and future-state capabilities.
- Identify cross-platform impacts, dependencies, risks, and trade-offs associated with major business and technology initiatives.
- Guide enterprise-level technology decisions and ensure architectural coherence across business capabilities, platforms, data domains, applications, and infrastructure.
- Lead architectural analysis and best practice supporting strategic planning, investment prioritization, technology evaluation, and build-vs-buy recommendations.
- Data Governance and Enterprise Information Architecture
- Provide architectural leadership to maximize data readiness for scaling AI, enterprise data governance, including data ownership, stewardship, data quality, metadata management, lineage, retention, information classification, data catalog capabilities, and governance tooling.
- Define and mature enterprise information architecture capabilities, including business glossaries, data dictionaries, taxonomies, conceptual models, logical models, domain models, and subject-area architectures.
- Partner with business stakeholders, data stewards, privacy teams, and technology teams to improve data ownership, data security, accountability, stewardship, discoverability, trust, and reuse.
- Integrate data governance and information architecture into architecture standards, delivery processes, platform strategies, security and privacy practices, integrity, compliance requirements, and enterprise decision forums.
- Treat information as a strategic enterprise asset and ensure architecture decisions reflect business vocabulary, data trust, data quality, and governed reuse.
- AI Architecture and Responsible AI Governance
- Define and evolve enterprise AI architecture strategy, AI architecture roadmap, AI governance services, and architectural guardrails for AI adoption and scaling.
- Establish architectural guidance for model management, vector database architectures, semantic search and retrieval capabilities, agent development frameworks and orchestration platforms, MCP architecture and governance, and end-to-end AI platform governance.
- Partner with the AI CoE, security, privacy, legal, and risk stakeholders to ensure AI architecture decisions align with enterprise guardrails, responsible AI expectations, and operational control needs.
- Evaluate emerging AI technologies, vendors, standards, and architecture approaches, providing enterprise adoption recommendations in alignment to enterprise strategy that balance business capability, platform fit, integration complexity, scalability, cost, and implementation risk.
- Data, AI, and Platform Architecture Alignment
- Develop architecture guidance for operational, analytical, and AI-oriented data platforms, including data lakes, lakehouses, data products, distributed data ecosystems, streaming architectures, and event-driven architectures.
- Define principles and standards for data integration, master data, metadata management, information sharing, interoperability, APIs, integrations, event management, data pipelines, and ML pipelines.
- Shape platform roadmaps and long-term capability evolution for assigned business platforms and strategic initiatives.
- Facilitate cross-domain decision-making where solutions impact multiple platforms, functions, data domains, AI capabilities, or organizational boundaries.
- Ensure platform decisions align to enterprise architecture principles, future-state strategies, data governance standards, AI governance standards, security, privacy, and compliance requirements.
- Architecture Governance and Decision Influence
- Participate in and help mature architecture governance activities, architecture reviews, design reviews, enterprise decision forums, and Architecture Review Board practices.
- Define and maintain architecture principles, policies, standards, exception governance, technical debt governance, reference models, and decision-support artifacts.
- Improve enterprise decision quality by providing clear architectural options, trade-offs, risks, implications, and recommendations for senior leaders and delivery teams.
- Ensure governance enables innovation and practical outcomes while maintaining enterprise alignment and avoiding unnecessary bureaucracy.
- Architecture Practice Leadership
- Facilitate cross-domain decision making where solutions impact multiple platforms, functions, or organizational boundaries.
- Support development and continuous improvement of REI’s architecture practice, architecture methods, architecture standards, repositories, knowledge management, and architecture tooling in partnership with the Enterprise Architecture community
- Maintain and improve architecture repositories and decision-support assets such as capability maps, data/domain models, architecture diagrams, reference architectures, and current/future-state architecture views.
- Mentor architects and engineering leaders across architecture disciplines and contribute to onboarding, interviewing, architecture community development, training, and best-practice sharing.
- Foster a collaborative architecture culture focused on business value, practical outcomes, clear decision-making, enterprise coherence, and long-term impact.
Required Qualifications
- Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Management, or a related field, or equivalent experience.
- 10+ years of experience across software engineering, solution architecture, enterprise architecture, data architecture, AI/ML systems, and enterprise platform leadership.
- 10+ years systems architecture experience with large-scale, mission-critical databases and applications.
- Demonstrated Enterprise Architecture experience influencing strategy and technology decisions across multiple business domains, platforms, and stakeholder groups.
- Experience building and modernizing large-scale retail technology solutions.
- Deep understanding of enterprise architecture disciplines, including business architecture, information architecture, application architecture, data architecture, technology architecture, and infrastructure architecture.
- Experience defining target architectures, roadmaps, reference architectures, architecture principles, standards, governance processes, and architecture artifacts.
- Strong experience with enterprise data architecture and governance concepts, including data lakes, lakehouses, data mesh concepts, data integration, metadata management, master data, data governance, data quality, lineage, stewardship, and information classification.
- Experience creating or governing enterprise information architecture capabilities such as business glossaries, data dictionaries, taxonomies, conceptual information models, logical models, domain models, and subject-area architectures.
- Experience designing, evaluating, or governing AI-enabled technology solutions, including modern AI architecture patterns such as Retrieval-Augmented Generation, agentic architectures, vector search, semantic retrieval, model orchestration, AI enablement platforms, and AI governance concepts.
- Exceptional communication, facilitation, influencing, decision-making, and stakeholder-management skills, with the ability to operate effectively with senior leaders, business stakeholders, architects, engineers, data teams, security, privacy, legal, risk, and delivery teams.
- Working knowledge and practical experience with established enterprise architecture frameworks (e.g., TOGAF, Zachman, FEAF).
- DAMA CDMP certification or equivalent demonstrated expertise in data management, data governance, metadata/reference data, data quality, data architecture, data integration/interoperability, data security, and data modeling/design.
- Experience applying AI governance, responsible AI, AI risk management, or AI management-system practices informed by frameworks such as NIST AI RMF, the NIST Generative AI Profile.
- Experience establishing or leading enterprise data governance programs, data catalog capabilities, metadata management, lineage, stewardship, data quality, information classification, or governance tooling.
- Experience with modern data platforms such as Snowflake, Databricks, lakehouse/warehouse environments, streaming platforms, analytics tooling, or AI/ML enablement platforms.
- Experience with architecture repositories and enterprise architecture management platforms such as LeanIX.
- Implementation experience with agentic AI, LLM architectures, Agentic Commerce, responsible AI controls, MLOps, privacy frameworks, compliance frameworks, or AI operating models.
- Willing and able to work from Seattle area office for collaborative one-off meetings such as whiteboarding sessions. No set in office requirement; just as needed.
Sponsorship
Must be legally authorized to work in the US. Employer will not sponsor position for employment visa status now or in the future (ex. H-1B).
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Closing: At REI, we believe the outdoors is for all. We are committed to becoming a fully inclusive, anti-racist, multicultural organization. We know that there's strength in our diversity – that each employee brings unique skills, experiences, and perspectives. Every day you are driving change, fostering a culture of respect, and knowing you're backed by benefits that support your whole life. To work towards this commitment and fulfill our brand promise of inspiring and enabling a life outside for everyone, we seek employees who demonstrate different ways of working, create a sense of belonging, and actively listen and learn. Pay Transparency We are committed to practices that promote pay equity and transparency. As required by applicable Pay Transparency laws, REI provides a range of compensation for roles that may be hired in locations under these requirements. Factors that may be used to determine your actual salary may include a wide array of factors, including: your specific skills and experience, geographic location or other relevant factors.
REI offers all regular employees a generous employee discount, access to health benefits, a retirement savings plan and accrued time off. Click here for a detailed overview of benefits plans by employee profile. Base Pay Range: $144,000.00 - $244,800.00 per year
Salary Context
This $144K-$244K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At REI, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($194K) sits 8% below the category median. Disclosed range: $144K to $244K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
REI AI Hiring
REI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $244K - $244K.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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