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About This Role
At GE Appliances, a Haier company, we come together to make “good things, for life.” As the fastest\-growing appliance company in the U.S., we’re powered by creators, thinkers and makers who believe that anything is possible and that there’s always a better way. We believe in the power of our people and in giving them the freedom to explore, discover and build good things, together.
The GE Appliances philosophy, backed by three simple commitments defines the way we work, invent, create, do business, and serve our communities: *we come together*, *we always look for a better way*, and *we create possibilities*.
Interested in joining us on our journey?
The Senior Principal Enterprise Data Architect – AI Data Transformation will serve as a strategic partner and governance leader within the Enterprise Architecture (EA) team of our global enterprise. This role combines advanced enterprise data architecture discipline with deep expertise in Artificial Intelligence infrastructure and Data Science enablement to plan, design, deploy, and execute technology solutions aligned to the organization's strategic roadmap.
The incumbent will be instrumental in operationalizing complex initiatives by significantly enhancing, evolving, and optimizing the enterprise data layer to make every data asset—across our global operations, supply chain, customer touchpoints, and connected products—AI\-ready, AI\-consumable, and AI\-trustworthy. This role will champion EA and AI data governance frameworks, drive Hoshin goal attainment, and serve as a key liaison between IT, business operations, product engineering, data science teams, and the EA team to ensure technology investments are aligned to enterprise standards and strategic AI objectives.Position
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Senior Principal Enterprise Data Architect, AI Data TransformationLocation
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USA, Louisville, KYHow You'll Create Possibilities
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AI Data Layer Enhancement \& Transformation (40%)
- Lead the architectural enhancement and evolution of the enterprise data layer, applying AI\-first design principles to unify data across the enterprise value chain (R\&D, supply chain, operations, and customer experience).
- Define, publish, and maintain the Enterprise AI Data Architecture Blueprint—the authoritative reference governing how data flows from source systems (e.g. ERP, CRM, PLM, IoT platforms) through transformation layers to AI models and business outcomes.
- Design and operationalize an Enterprise AI Data Readiness Framework that continuously assesses, scores, and improves data assets across five core dimensions: Completeness, Consistency, Timeliness, Representativeness, and Fairness.
- Architect and deploy enterprise\-grade vector database infrastructure and build enterprise embedding pipelines that transform structured records, enterprise documents, product manuals, and operational logs into high\-quality vector representations.
- Define the complete data architecture for Large Language Model (LLM) integration, including Retrieval\-Augmented Generation (RAG) architecture to support enterprise copilots, customer service, and operational workflows.
- Design ultra\-low latency data serving architectures and event\-driven AI data pipelines that feed live AI models in production (e.g., real\-time operational analytics, predictive maintenance, and customer insights).
- Establish an enterprise Synthetic Data Generation capability to augment scarce datasets, generate privacy\-safe alternatives to sensitive data, and simulate operational edge cases.
Enterprise Architecture Strategy \& Governance (35%)
- Serve as a strategic partner and governance leader within the EA team, applying and evolving enterprise architecture frameworks (TOGAF, Zachman) with AI\-era extensions tailored for a large\-scale, complex enterprise environment.
- Architect modern cloud data warehouse and Lakehouse solutions (e.g. BigQuery) as the unified, ACID\-compliant foundation for both analytical and AI/ML workloads on a single governed storage layer.
- Define and enforce data contracts between data producers (e.g., business operations, product engineering) and AI consumers across all domains to ensure schema, quality, freshness, and semantic consistency.
- Lead Master Data Management (MDM) strategy with AI entity resolution, enrichment, and disambiguation capabilities embedded in the MDM layer (covering Product, Material, Supplier, and Customer domains).
- Govern metadata management, data cataloging, and data lineage (e.g. Collibra) and design semantic/context data layers/Knowledge Graph infrastructure to map complex relationships between enterprise assets, suppliers, and business processes.
- Facilitate Architecture Review Board (ARB) processes for data and AI initiatives, ensuring alignment between project delivery and architectural intent.
- Align all data architecture decisions with regulatory and compliance requirements without compromising AI agility.
Data Science Enablement \& Stakeholder Engagement (15%)
- Apply statistical expertise to validate data representativeness, distributions, class balance, and sampling strategies for AI training datasets (e.g., ensuring datasets accurately represent real\-world operational realities).
- Serve as a trusted advisor and primary point of contact for business and IT stakeholders on AI data\-governed initiatives.
- Build and maintain effective working relationships at all levels of DT Staff, Extended DT Staff, and business leadership.
- Proactively identify risks, issues, dependencies, and bottlenecks; implement mitigation strategies to keep teams moving forward.
- Partner with functional/business teams, DT teams, and other team members to solve problems collaboratively and deliver project objectives.
Data Engineering Oversight \& Standards (10%)
- Provide architectural oversight and define enterprise standards for AI/ML\-optimized data pipelines, guiding data engineering delivery teams from raw ingestion through feature engineering.
- Define the architecture and integration patterns for the Enterprise Feature Store as the central hub of reusable, versioned ML features.
- Establish DataOps and pipeline governance frameworks, guiding delivery teams on best practices for CI/CD, automated data quality testing gates, and infrastructure\-as\-code.
- Define architectural patterns for streaming and event\-driven technologies to support high\-velocity enterprise and IoT telemetry data.
- Elicit detailed business and architecture requirements, translating them into clear architectural guidelines and actionable work items for data engineering teams.
What You'll Bring to Our Team
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Education:
- Bachelor's degree in Computer Science, Data Science, Information Systems, Mathematics, Engineering, or a related technical field required.
- Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field strongly preferred.
Experience and Qualifications:
- 15\+ years of progressive experience in data\-related roles, with a minimum of 5 years in Enterprise Data Architecture at enterprise scale.
- 3\+ years of experience designing and architecting AI/ML data infrastructure (feature stores, vector databases, model serving layers, semantic layers).
- Proven track record of leading enterprise data transformation programs with measurable AI and ML outcomes delivered in production environments.
- Enterprise Industry Experience: Prior experience architecting data solutions involving complex supply chains, ERP (SAP/Oracle), PLM, or large\-scale IoT/telemetry is preferred.
- Excellent oral and written presentation Skills
- Works independently with limited supervision and operates autonomously
- Working knowledge of enterprise architecture frameworks (e.g., TOGAF, Zachman)
Preferred Qualifications
- Experience working in both Agile and Waterfall delivery environments
- Project Management Professional (PMP) certification preferred
- TOGAF or other EA framework certification preferred
Our Culture
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Our work is centered on our People and Culture as reflected in our Zero Distance philosophy and we recognize the importance of reaffirming our commitment to inclusion and diversity (I\&D). This underscores our commitment to fostering an environment where every individual feels valued, connected, and empowered to contribute, while positioning our organization to adapt seamlessly to the evolving needs of our workforce and communities.
This reflects our dedication to creating solutions that: Empower colleagues by fostering an environment where all voices are heard, valued, and encouraged to contribute. Strengthen communities where we live and work. Reinforce a culture of belonging, purpose, and engagement. Reflect the diversity of the communities we serve through our workforce, products, and practices.
By further embedding Zero Distance into our People and Culture framework, we will continue to build a deeply connected organization. We are cultivating a culture of engagement, belonging, and connection, because while attracting new talent remains a priority, retention is a cornerstone of our strategy.
GE Appliances is a trust\-based organization. It is important we offer our employees the flexibility they need to do their best work while balancing the needs of the business and individuals. When you join GE Appliances, you will have the opportunity to work with your leader to create a flexible work arrangement that balances the needs of the individual, team, and organization.
GE Appliances is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.
GE Appliances participates in E\-Verify and will provide the federal government with your Form I\-9 information to confirm that you are authorized to work in the U.S
*If you are an individual with a disability and need assistance or an accommodation to use our website or to apply, please send an e\-mail* *to ask.recruiting@geappliances.com*
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At GE Appliances, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
GE Appliances AI Hiring
GE Appliances has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Louisville, KY, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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