Software Engineer AI/ML

GE Aerospace is seeking an AI Engineer to join their CES Business Intelligence team, which focuses on developing AI-powered solutions for commercial operations. The role involves transforming operational data into machine learning pipelines and applications, while collaborating with analytics teams and executive stakeholders to drive AI strategy and product development.

Responsibilities:

• 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

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