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About This Role
Job Description Summary
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The CES Business Intelligence team is building the next generation of AI\-powered solutions for commercial, contracts, and operations. We're looking for an AI Engineer to help transform GE Aerospace operational data into production\-grade machine learning pipelines, models, and LLM\-powered applications.
This is a multi\-faceted engineering role. You'll spend most of your time developing AI/ML products by training models, developing applications, and creating APIs. You will partner closely with analytics teams to enable AI within our existing operational tools. You'll also contribute to AI strategy and partner with executive stakeholders to align on requirements, success metrics, and business impact. We're looking for someone who's excited to expand their technical skillset in AI/ML and deliver advanced solutions that directly impact daily operations.
What you'll do: Design, build, deliver, and maintain AI/ML products including LLM\-powered applications, forecasting models, anomaly detection systems, and intelligent agents. Own the full AI/ML lifecycle: requirements analysis, model design, training, evaluation, API development, deployment, and operational support. Convert complex operational datasets into scalable AI capabilities that enable real\-time decision support.Job Description
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Roles and Responsibilities:
AI/ML Product Development
- Define, build, and evolve AI\-powered software products that accelerate Commercial Engine Services operations—including LLM applications, machine learning models, and intelligent automation for supply chain optimization
- Create Model Context Protocol (MCP) servers that package domain\-specific AI capabilities for reuse across the enterprise.
- Package AI/ML models as robust, well\-documented APIs that enable seamless integration into dashboards, applications, and operational workflows.
- Collaborate with BI team to embed AI features into existing applications that enable natural language queries, predictive insights, and intelligent recommendations directly within user\-facing applications
Technical Leadership \& Collaboration
- Provide hands\-on AI/ML technical leadership for our modernization initiative, setting best practices for prompt engineering, model evaluation, experiment tracking, and responsible AI development
- Partner with executive stakeholders and BI leadership to understand business challenges and translate operational needs into AI/ML capabilities
- Ensure AI/ML models deploy reliably to AWS infrastructure with proper monitoring, logging, and performance optimization
- Translate requirements into a prioritized backlog of AI/ML products, driving delivery to required timelines, quality standards, and measurable business outcomes
- Collaborate with data platform teams to design data pipelines that feed AI/ML models to ensure data quality, freshness, and proper feature engineering from the Databricks medallion architecture
AI/ML Infrastructure \& MLOps
- Establish MLOps practices including experiment tracking (MLflow, Weights \& Biases), model versioning, automated evaluation pipelines, and A/B testing frameworks for continuous model improvement
- Drive world\-class quality through rigorous SDLC practices: Lean/Agile/XP, CI/CD, automated testing, secure coding, scalability patterns, documentation\-as\-code, refactoring, and performance engineering
- Implement monitoring and observability for AI/ML systems to track model performance, data drift, prediction latency, and error rates; build automated alerting for model degradation
- Design vector database architectures and semantic search capabilities to power RAG applications; optimize retrieval strategies for accuracy and latency
- Build evaluation frameworks for LLM applications—measuring response quality, accuracy, relevance, and hallucination rates; establish automated testing for prompt templates and model outputs
- Ensure responsible AI practices including bias detection, explainability (SHAP, LIME), privacy\-preserving techniques, and compliance with enterprise AI governance policies
Innovation \& Strategy
- Drive the AI/ML roadmap for Commercial Engine Services BI team by identifying high\-impact use cases, evaluating emerging AI technologies, and building proof\-of\-concepts that demonstrate business value
- Stay current on LLM advancements, ML frameworks, vector databases, and AI application patterns; bring practical innovations that improve decision speed and operational outcomes
- Engage domain experts to ensure successful transfer of complex operational knowledge into AI models and intelligent systems
- Establish reusable AI/ML components, templates, and reference architectures that accelerate future development and enable the BI team to leverage AI capabilities independently
- Communicate AI/ML concepts, tradeoffs, and results to non\-technical stakeholders through clear documentation, executive presentations, and live demonstrations
Required Qualifications
- Bachelor's Degree in Computer Science, Data Science, Statistics, Engineering, or related field from an accredited college or university
- Minimum of 3 years of hands\-on AI/ML engineering experience building and deploying machine learning models and/or AI\-powered applications to production
Desired Characteristics
Technical Expertise
- Write production\-quality code that meets standards and delivers intended functionality using the most appropriate technologies for the project (e.g., Python, Java, C\#, TypeScript—based on system needs)
- Proven experience building data platforms and production LLM\-powered applications; strong understanding of prompt engineering, retrieval\-augmented generation, and vector databases
- Strong foundation in supervised/unsupervised learning, time\-series forecasting, classification, and optimization
- Experience with MLflow, model registries, automated training pipelines, A/B testing frameworks, and model monitoring; strong DevOps collaboration skills
- Expertise in development platforms and services: AWS, Visual Studio, Databricks, GitHub, etc.
- Experience building REST APIs (FastAPI, Flask) for model serving; understanding of authentication, rate limiting, versioning, and API documentation
Domain \& Business Acumen
- Experience building AI/ML solutions for supply chain, manufacturing, maintenance, or operations analytics is a strong plus
- Understands business metrics and can translate AI/ML capabilities into quantifiable business outcomes (cost savings, time reduction, forecast accuracy improvement)
- Skilled in breaking down ambiguous AI problems, writing clear problem statements, and estimating model development effort accurately
- Stays current on AI/ML industry trends (LLM advancements, new frameworks, emerging techniques); brings practical innovations backed by proof\-of\-concepts
Leadership \& Collaboration
- Leads by example through delivering AI/ML products while mentoring team on AI integration, prompt engineering, and model usage
- Able to work through ambiguity and drive alignment between AI capabilities and business needs; communicates model limitations, confidence intervals, and uncertainty clearly to non\-technical stakeholders
- Continuously measures solutions against user expectations while balancing competing priorities and maintaining build quality.
Personal Attributes
- Strong written and verbal communication skills with the ability to explain complex AI/ML concepts simply and translate effectively between data scientists, software engineers, and business stakeholders
- Effective collaborator who works seamlessly with BI developers, platform engineers, and business stakeholders
- Business\-minded approach that focuses on operational metrics, user needs, and business impact while designing AI solutions that solve real problems rather than technical exercises
- Persists to completion by driving AI/ML products through deployment, monitoring, and iteration while taking ownership of model performance and continuously improving accuracy
The base pay range for this position is $112,000\-150,000\. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set. This position is also eligible for an annual discretionary bonus based on a percentage of your base salary/ commission based on the plan. This posting is expected to close on May 28th, 2026\.
GE Aerospace offers comprehensive benefits and programs to support your health and, along with programs like HealthAhead, your physical, emotional, financial and social wellbeing. Healthcare benefits include medical, dental, vision, and prescription drug coverage; access to a Health Coach from GE Aerospace; and the Employee Assistance Program, which provides 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Aerospace Retirement Savings Plan, a 401(k) savings plan with company matching contributions and company retirement contributions, as well as access to Fidelity resources and planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability insurance, life insurance, and paid time\-off for vacation or illness.
GE Aerospace (General Electric Company or the Company) and its affiliates each sponsor certain employee benefit plans or programs (i.e., is a “Sponsor”). Each Sponsor reserves the right to terminate, amend, suspend, replace or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a Sponsor’s welfare benefit plan or program. This document does not create a contract of employment with any individual.
\#LI\-JR1
Additional Information
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GE Aerospace offers a great work environment, professional development, challenging careers, and competitive compensation. GE Aerospace 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 Aerospace will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).
Relocation Assistance Provided: No
\#LI\-Remote \- This is a remote position
Salary Context
This $112K-$150K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $190K across 219 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At GE Aerospace, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 797 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($131K) sits 44% below the category median. Disclosed range: $112K to $150K.
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 Aerospace AI Hiring
GE Aerospace has 3 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span KY, US, Evendale, OH, US, Remote, US. Compensation range: $140K - $210K.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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