[Hiring] Software Engineer AI/ML @GE Aerospace

Role Description

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.

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.

Requirements

• 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

• Healthcare benefits include medical, dental, vision, and prescription drug coverage.
• Access to a Health Coach from GE Aerospace.
• Employee Assistance Program providing 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.
• Access to Fidelity resources and planning consultants.
• Tuition assistance, adoption assistance, paid parental leave, disability insurance, life insurance, and paid time-off for vacation or illness.