Software Engineer AI/ML

GE Aerospace•Evendale, OH

1d•$112,000 - $150,000•Remote

About The Position

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.

Requirements

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
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
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
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.
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

Responsibilities

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.
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.
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.
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.
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.

Benefits

medical, dental, vision, and prescription drug coverage
access to a Health Coach from GE Aerospace
Employee Assistance Program
GE Aerospace Retirement Savings Plan, a 401(k) savings plan with company matching contributions and company retirement contributions
access to Fidelity resources and planning consultants
tuition assistance
adoption assistance
paid parental leave
disability insurance
life insurance
paid time-off for vacation or illness