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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?
We are looking for a highly motivated Senior Applied AI Engineer to join our digital innovation team and help revolutionize how we serve our primarily B2C (Business\-to\-Consumer) customers. As a major appliance manufacturer, we’re committed to delivering seamless experiences for consumers across our contact center channels, including voice, chat, and messaging. This role is ideal for someone who is passionate about AI, automation, and customer service, with a hands\-on approach to creating and optimizing use cases / agents for AI\-driven interactions.
You’ll be responsible for designing, building and supporting scalable software solutions that power contact center operations and customer engagement. Working as an individual contributor within a cross\-functional team of engineers, architects, and program leadership to deliver reliable, high\-quality solutions and transactional capabilities, such as order management, scheduling repairs, processing warranty claims, and assisting with product troubleshooting. You will collaborate closely with senior architects on system design, partner with peer engineers on implementation and code quality, and provide guidance and mentorship to junior developers. You will contribute to technical decision\-making, drive best practices in development and operations, and ensure solutions meet performance, security, and business requirements.
This position is headquartered in Louisville, KY with availability to work remotely.Position
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Senior Applied AI EngineerLocation
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USA, Louisville, KYHow You'll Create Possibilities
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AI Agent Development \& Implementation
- Design, build, and maintain AI\-driven conversational agents supporting customer engagement use cases, from FAQs to transactional workflows (e.g., order management, repair scheduling, troubleshooting)
- Develop and implement conversation flows, decision trees, and dialog management logic aligned to defined architectures and best practices
- Craft and optimize AI copilots and other employee\-facing agents to drive efficiency and strengthen process adherence.
- Partner with architects, product owners, and domain experts to translate business requirements into scalable, maintainable AI solutions
- Contribute to the evolution of development standards, reusable components, and implementation patterns for AI agents
Agent Tuning \& Optimization
- Configure, train, and tune AI/NLP models to improve intent recognition, entity extraction, and response quality
- Analyze agent performance metrics and interaction data to identify gaps and implement targeted improvements
- Support testing efforts, including user acceptance testing and iterative refinement based on real\-world feedback
Transactional Flows \& System Integration
- Implement and support transactional conversational flows that integrate with backend systems (e.g., CRM, ERP, telephony)
- Develop and maintain APIs and service integrations that enable AI agents to perform real\-time actions and retrieve customer data
- Ensure solutions meet security, privacy, and compliance requirements when handling customer data and executing transactions
Cross\-Functional Collaboration
- Collaborate with development peers, architects, and program management to deliver high\-quality solutions within established timelines
- Work with operations and customer experience teams to understand pain points and support the delivery of AI\-driven improvements
- Provide technical guidance and mentorship to junior engineers and contribute to team knowledge sharing
Operational Support \& Continuous Improvement
- Monitor and troubleshoot production issues, ensuring reliability and performance of AI\-driven systems
- Contribute to observability tracking of AI agent performance (e.g., containment, accuracy, customer satisfaction)
- Continuously improve code quality, test coverage, and deployment practices through CI/CD and DevOps standards
- Stay current with emerging AI, NLP, and contact center technologies and apply relevant advancements to ongoing work
What You'll Bring to Our Team
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- Bachelor’s or Master’s degree in Computer Science, Engineering or a related field.
- 5 years of relevant software engineering experience, including at least 1 year working with AI use cases such as conversational AI or natural language processing in customer\-facing solutions
- Proficiency with at least one cloud platform (e.g., AWS, Azure, GCP), particularly for event\-driven and real\-time architectures
- Hands\-on experience with scripting languages (e.g., Python, JavaScript) to build integrations, enhance agent functionality, and support transactional workflows
- Proficiency in designing conversational flows, decision trees, and systems that support customer inquiries and transactions
- Experience integrating contact center platforms with CRM systems (e.g., Salesforce, Dynamics, ServiceNow), including screen pops, case workflows, and customer data synchronization
- Hands\-on experience developing and optimizing AI chatbots or virtual assistants, including model training, conversation design, and performance improvement
- Strong problem\-solving skills with the ability to design solutions that balance customer experience and business objectives
- Experience with CI/CD pipelines and DevOps practices, including automated testing and deployment
- Strong organizational skills with the ability to manage multiple priorities and deliverables
- Effective communication skills, with the ability to translate technical concepts for non\-technical stakeholders
- Experience using work management tools such as Jira, Smartsheet, or similar to manage backlogs, track tasks/issues, coordinate dependencies, and communicate project status.
Preferred Qualifications:
- Experience working with a B2C contact center, especially in a consumer goods or appliance manufacturer environment.
- Hands\-on experience integrating with Salesforce (e.g., Service Cloud, CTI adapters, APIs, event streams) to support agent workflows and customer data access
- Familiarity with telephony services such as Amazon Connect, Genesys, NICE, Five9, or similar platforms
- Knowledge of NLP models and how they can be fine\-tuned for specific customer service applications.
- Background in customer journey mapping, process automation, or integrating AI with CRM systems (e.g., Salesforce).
- Passionate about continuous learning, especially in the AI/ML field, and driven by results.
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 [email protected]*
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
GE Appliances AI Hiring
GE Appliances has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Louisville, KY, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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