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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 skilled and motivated Senior AI Engineer to join our enterprise AI platform team. In this role, you will design, develop, and operate core AI platform services that enable secure, scalable, and reliable AI agents and applications. The Senior AI Engineer will work closely with senior engineers and architects to deliver production\-grade platform capabilities while mentoring junior engineers and owning features end\-to\-end.Position
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Senior AI EngineerLocation
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USA, Louisville, KYHow You'll Create Possibilities
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- Work with Senior Software Engineers and Principal Architects to design, build, and operate core AI platform services.
- Develop, test, and maintain backend services supporting AI agent execution, orchestration, and tool integration.
- Implement RESTful and event\-driven APIs that enable agents to interact with enterprise systems in a secure and auditable way.
- Contribute to the implementation of a centralized tool gateway/mediation layer used by AI agents.
- Integrate enterprise data platforms, APIs, and services through well\-defined contracts and interfaces.
- Deploy and operate services on AWS and Google Cloud with a focus on reliability, scalability, and security.
- Define and follow best practices for containerization and orchestration (Docker, Kubernetes, Helm).
- Establish, maintain, and improve CI/CD pipelines and infrastructure\-as\-code workflows.
- Implement logging, metrics, tracing, and alerts to ensure platform observability.
- Participate in incident response, troubleshooting, and root cause analysis for platform services.
- Mentor junior engineers through code reviews, pairing, and technical guidance.
- Collaborate cross\-functionally with AI engineers, data engineers, security teams, and product partners.
- Participate in Agile ceremonies, including sprint planning, stand\-ups, retrospectives, and backlog grooming.
What You'll Bring to Our Team
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- Bachelor’s degree in computer science, software engineering, or a related field
- 5\+ years of experience in backend software engineering, cloud engineering, or platform development
- Strong expertise in Python programming
- Hands\-on experience building and operating services on AWS and/or Google Cloud
- Solid understanding of microservices architecture, RESTful APIs, and event\-driven systems
- Experience with SQL and NoSQL databases (e.g., PostgreSQL, DynamoDB, MongoDB)
- Experience with DevOps tools and practices, including GitHub, CI/CD pipelines, Docker, Kubernetes, and Terraform
- Ability to independently own and deliver engineering tasks and small projects.
- Experience working in Agile/SCRUM teams
- Strong problem\-solving skills and ability to work in complex systems
Preferred Qualifications:
- Exposure to AI/ML platforms, LLM\-based systems, or agent frameworks
- Experience building or integrating API gateways or mediation layers
- Experience with messaging and streaming systems (Kafka, Kinesis, Pub/Sub)
- Familiarity with serverless architectures (AWS Lambda, Google Cloud Functions)
- Knowledge of cloud security best practices (IAM, encryption, secrets management)
- Experience with observability tools such as Prometheus, Grafana, or ELK
- Prior experience mentoring junior engineers or acting as a technical lead on small initiatives
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 3,708 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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