Software Engineer AI/ML

GE Aerospace is building the next generation of AI-powered solutions for commercial, contracts, and operations. They are seeking an AI Engineer to transform operational data into production-grade machine learning pipelines and applications, while collaborating with analytics teams to enable AI within existing tools.

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